CATTLEGUARD: AI-POWERED UAV FOR CATTLE THEFT PREVENTION
INTRODUCTION:
The increasing demand for dairy products and beef has made cattle farming a lucrative business. However, this profitability comes with challenges, particularly the growing issue of cattle theft. Traditional methods of monitoring cattle farms, such as manual supervision and basic surveillance systems, are often insufficient and labor-intensive. As cattle farms expand, the complexity of maintaining security also increases, necessitating the adoption of advanced technological solutions. Our project aims to address this issue by leveraging deep learning and UAV (Unmanned Aerial Vehicle) technology to provide a comprehensive and automated cattle theft identification system.
Utilizing state-of-the-art convolutional neural networks (CNNs), our system is designed to detect anonymous activities in cattle farms with high accuracy. By integrating UAVs for aerial surveillance, we can continuously monitor large areas and capture real-time data. The system processes these data using advanced CNN architectures, identifying suspicious individuals and alerting farm owners immediately. This approach not only enhances the security of cattle farms but also reduces the need for constant manual oversight, allowing farmers to focus on other critical aspects of farm management.
PROBLEM STATEMENT:
Cattle theft is a significant issue for farmers, resulting in substantial financial losses and threatening livelihoods. Traditional methods of monitoring, such as manual oversight and basic surveillance, are often ineffective, especially in large farming areas. Thieves frequently employ tactics like masking their faces and moving stealthily to avoid detection, further complicating prevention efforts. There is an urgent need for an automated, reliable, and efficient system that can provide continuous and accurate monitoring to detect and prevent cattle theft, ensuring the security of livestock and reducing the burden on farmers.
GOALS:
The primary goal of this project is to develop an automated cattle theft identification system that significantly enhances the security of cattle farms. This system aims to utilize UAVs for comprehensive surveillance, capturing real-time aerial footage of the farm. By employing advanced CNN architectures, the system will analyze this footage to detect any suspicious activities or individuals. Upon detection, the system will immediately notify farm owners and managers, providing them with precise information about the location and nature of the detected threat. Ultimately, this project seeks to reduce the incidence of cattle theft, minimize financial losses for farmers, and improve overall farm management through the implementation of cutting-edge technology.
PRIOR RESEARCH:
Various studies have explored methods to prevent cattle theft and improve livestock monitoring, each presenting unique approaches and facing distinct limitations. Dieng et al. investigated IoT solutions for preventing cattle rustling in Africa, highlighting the potential of real-time monitoring and tracking through IoT technology to reduce theft incidents. However, the reliance on GPS and wireless nodes often suffers from short-range limitations and inefficiencies when cattle move far from the ranch. Similarly, Sundaramoorthi et al. proposed an IoT-based animal health and theft prevention model that provides real-time information on the animal's status, but it only notifies when the cattle leave the farm boundary without providing their precise location, which limits its effectiveness.
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Advanced methods incorporating deep learning have also been proposed. Kim et al. developed a system using RGB background modeling and GMM to extract moving objects, classifying them based on skeletal characteristics and color similarity, but it struggles in low-light conditions. Other research, like that by Shojaeipour et al., focused on biometric identification via deep learning, which improves individual cattle recognition but doesn't directly address theft prevention. While these methods enhance cattle identification and provide some level of theft deterrence, they often fall short in offering comprehensive, real-time surveillance and immediate alert systems necessary to effectively prevent cattle theft on large farms.
NOVELTY:
The proposed system uniquely integrates UAVs with advanced CNN architectures (LeNet, VGG, GoogleNet, ResNet) to provide real-time, high-accuracy detection of suspicious activities on cattle farms. Unlike previous solutions, it offers comprehensive surveillance by capturing aerial footage and analyzing it for anonymous individuals, even in low-light conditions. This approach not only enhances detection capabilities but also ensures immediate notification to farm owners, significantly reducing response time and improving farm security.
PROPOSED SYSTEM OVERVIEW:
A UAV outfitted with a night vision camera that continuously patrols the cattle farm puts the idea into action. Images are simultaneously taken and stored in network-connected cloud services. As soon as a visual of an anonymous person is recorded on frames, our robust algorithm will identify the individual and their location. The UAV and our model are associated with an alarm system, and the prompt alarm will aid in minimizing the loss. Limiting further losses could be facilitated by taking immediate action or getting in touch with the manager or owner. Farmers can go to the indicated location, rectify any issues, and balance the farm instead of constantly manually inspecting the area.
By leveraging UAV technology and advanced deep learning algorithms, our system offers a comprehensive and efficient solution to the persistent problem of cattle theft. The UAV's continuous patrols and night vision capability ensure effective monitoring even in low-light conditions, while the cloud-based storage and analysis provide real-time detection and immediate alerts. This innovative approach not only enhances the security of cattle farms but also reduces the need for constant manual oversight, allowing farmers to focus on other critical aspects of farm management.
WORKING METHODLOGY
Dataset Collection:
The dataset was initially obtained from Kaggle, focusing on mask detection. This dataset was then expanded by creating our own dataset, which included images captured under various conditions such as low-light, top-view, bottom-view, and side-view. Additionally, images were manually cropped and augmented to improve the dataset. Further, images depicting thieves carrying cattle were sourced from the internet to test our model comprehensively. The final dataset thus consisted of both controlled and real-world images, enhancing the model's robustness.
Model Training:
LeNet:
LeNet, one of the earliest CNN architectures, was employed due to its simplicity and effectiveness in basic image classification tasks. This model comprises seven layers, including convolutional, subsampling, and fully connected layers. It was trained on our dataset to identify masked individuals in various conditions. Despite its relatively simple structure, LeNet achieved significant accuracy by learning essential features through convolution and pooling operations, proving effective in recognizing suspicious activities on cattle farms.
Visual Representation of Data flow in LeNet
VGG-16:
VGG-16, developed by the Visual Geometry Group at Oxford, is known for its depth and use of small 3x3 convolution filters. This architecture was chosen for its ability to capture fine-grained details through its 16 layers, including 13 convolutional layers and 3 fully connected layers. The pre-trained VGG-16 model was fine-tuned on our dataset, leveraging its deep structure to improve the detection of masked individuals. The model's extensive depth allowed it to learn complex features, contributing to its robustness in various environmental conditions.
Architecture of VGG16
GoogleNet:
GoogleNet, also known as Inception v1, was utilized for its efficiency and performance in handling large-scale image classification tasks. This model uses Inception modules, which allow the network to capture multi-scale features through parallel convolutions of different sizes. GoogleNet was trained on our dataset to detect anonymous individuals, achieving high accuracy with fewer parameters compared to other deep networks. Its innovative architecture, which balances depth and computational efficiency, made it particularly effective in processing the diverse and augmented dataset.
Illustration of GoogleNet Architecture
ResNet:
ResNet, or Residual Networks, addresses the vanishing gradient problem by introducing skip connections that allow gradients to flow directly through the network. ResNet-50, a version with 50 layers, was implemented to leverage its deep architecture for high-accuracy detection of masked individuals. This model was trained on our dataset, benefiting from its ability to learn intricate features without degradation. The use of residual blocks enabled the model to maintain high performance even with increased depth, making it highly suitable for the task of detecting suspicious activities on cattle farms.
Work flow of ResNet50 Model
Surveillance and Detection: UAVs equipped with night vision cameras continuously patrol the cattle farms, capturing aerial footage. This footage is transmitted to a cloud-based server where frames are extracted and analyzed in real-time. The trained CNN models process these frames to detect any suspicious activities, particularly focusing on identifying individuals attempting to mask their identities.
Alert System: Upon detecting an anonymous individual, the system triggers an alarm. The alert system promptly notifies the farm owner and manager, providing precise information about the location of the detected activity. This immediate notification enables quick response to potential theft incidents, allowing farmers to address the issue promptly and mitigate losses.Response and
Rectification:Farmers or designated personnel can then proceed to the indicated location to investigate and rectify any issues. This system not only ensures the security of the cattle but also reduces the need for continuous manual inspection, thereby optimizing farm management and enhancing overall efficiency. By integrating advanced surveillance technology with deep learning models, the proposed system provides a reliable and efficient solution for preventing cattle theft, ensuring real-time monitoring and immediate response to potential threats.
CHALLENGES ADDRESSED:
The proposed system tackles several key challenges associated with traditional cattle theft prevention methods. One of the primary challenges is the inefficiency of manual monitoring, which is labor-intensive and often fails to detect theft promptly. By deploying UAVs equipped with night vision cameras, the system ensures continuous, automated surveillance, significantly reducing the need for human oversight and enhancing the reliability of monitoring, even in low-light conditions.
Another challenge is the difficulty in detecting masked individuals or those attempting to conceal their identities. Traditional surveillance systems often struggle to recognize such individuals, especially in large and poorly lit areas. Our system addresses this by utilizing advanced CNN architectures like LeNet, VGG-16, GoogleNet, and ResNet, which are specifically trained to identify masked individuals and detect suspicious activities with high accuracy, regardless of environmental conditions.
The problem of timely response to potential theft incidents is also addressed by our system. Traditional methods often result in delays in detecting and responding to theft, leading to significant losses. The proposed system provides real-time alerts to farm owners and managers as soon as suspicious activity is detected. This immediate notification allows for rapid response, minimizing potential losses and enhancing the overall security of the cattle farm.
Additionally, the system overcomes the limitation of short-range and inefficiency faced by GPS and IoT-based solutions. By leveraging UAVs for aerial surveillance and cloud-based analysis, the system ensures comprehensive coverage of large farming areas and effective monitoring of cattle, regardless of their location within the farm. This holistic approach significantly enhances the ability to prevent theft and manage livestock more efficiently.
RESULTS AND DISCUSSION:
Initial Analysis of Performance Metrics:
The performance of the proposed cattle theft identification system was evaluated using key metrics such as precision, accuracy, and per-class accuracy for different CNN architectures. Precision measures the ability of the model to correctly identify true positives, while accuracy assesses the overall correctness of the model's predictions. Per-class accuracy provides insight into how well each class (e.g., masked vs. non-masked individuals) is detected.
The table below summarizes the performance metrics after the training phase for the different models used in the system:
GoogleNet: GoogleNet achieved the highest accuracy at 90%, with a precision of 85%. Its per-class accuracy was 70%, indicating a strong ability to correctly classify both masked and non-masked individuals. The use of Inception modules allowed GoogleNet to efficiently capture multi-scale features, contributing to its high performance.
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ResNet: ResNet's precision was lower at 50%, but it still managed an accuracy of 80%. The model's per-class accuracy was 60%. ResNet's deep residual connections helped mitigate the vanishing gradient problem, enabling it to maintain reasonable performance despite its depth.
VGG-16: VGG-16 achieved a precision of 60% and an overall accuracy of 69%. Its per-class accuracy was 64%, indicating moderate performance. The depth of VGG-16, with its multiple convolutional layers, allowed it to capture detailed features, though it required significant computational resources.
LeNet: LeNet performed well with a precision of 70% and an overall accuracy of 87%. Its per-class accuracy was 75%, demonstrating strong detection capabilities. Despite being an older architecture, LeNet's simplicity and effectiveness in learning essential features contributed to its high accuracy.​
The proposed system was tested using a diverse dataset that included real-time masked photographs, low-light images, and cropped versions to simulate various real-world conditions. The performance of different CNN architectures was evaluated based on their ability to detect masked individuals and suspicious activities. The following results summarize the findings:
GoogleNet:
GoogleNet showed the highest accuracy in detecting masked individuals across various conditions, achieving 85% accuracy in low-light images, 80.4% in cropped images, and 89% in original images. The use of Inception modules allowed GoogleNet to effectively capture multi-scale features, making it robust in diverse scenarios.
ResNet:
ResNet achieved 81.1% accuracy in low-light images, 66.7% in cropped images, and 77% in original images. The use of residual connections helped maintain performance, but further tuning may be required to improve precision.
VGG-16:
VGG-16 had moderate performance, with 61% accuracy in low-light images, 49% in cropped images, and 63% in original images. While VGG-16's depth helped capture detailed features, its performance was lower compared to GoogleNet and ResNet.
LeNet:
LeNet performed well, particularly in cropped images with an 88% accuracy. It achieved 79% accuracy in low-light images and 83% in original images. Despite being an older architecture, LeNet's simplicity and effectiveness contributed to its robust performance.
The analysis indicates that GoogleNet is the best-performing model overall, offering a balanced combination of precision, accuracy, and computational efficiency. LeNet also showed robust performance, making it a viable option for real-time detection. ResNet and VGG-16, while effective, had lower precision, suggesting a need for further tuning or integration with other methods to enhance their detection capabilities.
Overall, the integration of UAVs for continuous surveillance and the use of advanced CNN architectures provided a comprehensive solution to cattle theft detection. The real-time alert system ensures immediate notification to farm owners, allowing for rapid response and minimizing potential losses. Future work could focus on further refining the models, expanding the dataset with more diverse real-world scenarios, and integrating additional technologies to enhance system performance.
CONCLUSION:
The proposed cattle theft identification system effectively addresses the challenges associated with traditional methods of livestock monitoring and theft prevention. By leveraging UAVs equipped with night vision cameras and advanced CNN architectures, the system provides continuous, automated surveillance of cattle farms. This approach significantly reduces the need for manual oversight and enhances the accuracy and efficiency of theft detection.
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Our evaluation demonstrated that GoogleNet outperformed other models, achieving the highest accuracy across various conditions, including low-light and cropped images. LeNet also showed robust performance, particularly in detecting masked individuals in cropped images. While ResNet and VGG-16 were effective, they required further tuning to improve precision. The integration of these models with UAV technology ensures comprehensive coverage and real-time monitoring, providing farm owners with immediate alerts and precise information about suspicious activities.
The system's ability to detect and notify farm owners of potential threats in real-time minimizes response times and mitigates losses. This innovative solution not only enhances the security of cattle farms but also optimizes farm management by allowing farmers to focus on other critical tasks. Future work will focus on refining the models, expanding the dataset to include more diverse real-world scenarios, and integrating additional technologies to further enhance system performance.
In conclusion, the proposed cattle theft identification system offers a reliable, efficient, and scalable solution to the persistent problem of cattle theft, ensuring the security and welfare of livestock and supporting the livelihoods of farmers worldwide.
ACKNOWLEDGEMENT:
Gratitude is extended to Dr. BKSP Alluri for invaluable guidance and support throughout this project. Thanks are also due to Dr. Sumathi D for continuous encouragement, detailed feedback, and assistance in refining the research. Appreciation is extended to VIT-AP University for providing the necessary resources and environment to carry out this work.
REFERENCES:
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Dieng, O., Diop, B., Thiare, O., & Pham, C. (2017, March). A study on IoT solutions for preventing cattle rustling in African context. In ICC (Vol. 17, pp. 1-13).
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Xu, B., Wang, W., Guo, L., Chen, G., Li, Y., Cao, Z., & Wu, S. (2022). CattleFaceNet: A cattle face identification approach based on RetinaFace and ArcFace loss. Computers and Electronics in Agriculture, 193, 106675.
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Kim, J. H., & Joo, Y. H. (2018). Livestock Theft Detection System Using Skeleton Feature and Color Similarity. The Transactions of The Korean Institute of Electrical Engineers, 67(4), 586-594.
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Qiao, Y., Su, D., Kong, H., Sukkarieh, S., Lomax, S., & Clark, C. (2019). Individual cattle identification using a deep learning-based framework. IFAC-PapersOnLine, 52(30), 318-323.
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Shojaeipour, A., Falzon, G., Kwan, P., Hadavi, N., Cowley, F. C., & Paul, D. (2021). Automated muzzle detection and biometric identification via few-shot deep transfer learning of mixed breed cattle. Agronomy, 11(11), 2365.