Edge Machine Learning for Wildlife Conservation : Detection of Poachers Using Camera Traps

• Main Authors: Arnesson, Pontus, Forslund, Johan
• Format: Others
• Language: English
• Published: Linköpings universitet, Reglerteknik 2021
• Subjects: deep learning; ai; human detection; microcontroller; edge devices; camera trap; esp32; Control Engineering; Reglerteknik
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-177483

This thesis presents how deep learning can be utilized for detecting humans ina wildlife setting using image classification. Two different solutions have beenimplemented where both of them use a camera-equipped microprocessor to cap-ture the images. In one of the solutions, the deep learning model is run on themicroprocessor itself, which requires the size of the model to be as small as pos-sible. The other solution sends images from the microprocessor to a more pow-erful computer where a larger object detection model is run. Both solutions areevaluated using standard image classification metrics and compared against eachother. To adapt the models to the wildlife environment,transfer learningis usedwith training data from a similar setting that has been manually collected andannotated. The thesis describes a complete system’s implementation and results,including data transfer, parallel computing, and hardware setup. One of the contributions of this thesis is an algorithm that improves the classifi-cation performance on images where a human is far away from the camera. Thealgorithm detects motion in the images and extracts only the area where thereis movement. This is specifically important on the microprocessor, where theclassification model is too simple to handle those cases. By only applying theclassification model to this area, the task is more simple, resulting in better per-formance. In conclusion, when integrating this algorithm, a model running onthe microprocessor gives sufficient results to run as a camera trap for humans.However, test results show that this implementation is still quite underperform-ing compared to a model that is run on a more powerful computer.