Biodiversity Monitoring Using Machine Learning for Animal Detection and Tracking

Sammanfattning: As an important indicator of biodiversity and ecological environment in a region, the number and distribution of animals has been given more and more attention by agencies such as nature reserves, wetland parks, and animal protection supervision departments. To protect biodiversity, we need to be able to detect and track the movement of animals to understand which animals are visiting the space. This thesis uses the improved You Only Look Once Version 5 (YOLOv5) target detection algorithm and Simple online and real-time tracking with a deep association metric (DeepSORT) tracking algorithm to provide technical support for bird monitoring, identification and tracking. Specifically, the thesis tries different improvement methods based on YOLOv5 to solve the problem that small targets in images are difficult to detect. In the backbone network, different attention modules are added to enhance the network feature extraction ability; in the neck network part, the Bi-Directional Feature Pyramid Network (BiFPN) structure is used to replace the Path Aggregation Network (PAN) structure to strengthen the utilization of underlying features; in the detection head part, a high-resolution detection head is added to improve the detection ability of tiny targets. In addition, a better loss function has been used to improve the algorithm’s performance on small birds. The improved algorithms in this paper have been used in multiple comparative experiments on the VisDrone data set and a data set of bird flight images, and the results show that compared with the baseline using YOLOv5, for VisDrone data set, Spatial-to-Depth (SPD)-Convolutional stride-free (Conv) gets the highest training mean Average Precision (mAP) of all methods with an increase from 0.325 to 0.419; for the bird data set, the best result of training mAP that could be achieved is adding a P2 layer, which reaches an improvement from 0.701 to 0.724. After combining the You Only Look Once (YOLO) with DeepSORT to implement the tracking function, the improved method makes the final tracking effect better.