Abstract
Wildlife plays a very important role in the ecological balance of the earth. With the rapid development of computer vision, we can use object detection techniques to count the number of wildlife for their better conservation; however, some wildlife live mainly in groups, so the datasets are often distributed in a dense state. It is difficult in dense environments since boxes for different objects should be preserved and duplicate detections should be suppressed at the same time. It is challenging to detect dense wildlife. To solve the problem, we propose an improved model based on YOLOv5. Firstly, we add a Convolutional Block Attention Model(CBAM) to enable the network to extract the richer wildlife features. Secondly, Distance Intersection over Union-Non Maximum Suppression(DIoU-NMS) is used to solve the problem of multiple-objects overlap in wildlife detection to reduce the redundancy of the wildlife. Finally, Efficient Intersection over Union Loss(EIoU Loss) is used to speed up the convergence of the loss function. The experimental results show that our model achieved good performance on the public dataset Wildlife. Compared with the basic YOLOv5s, it reaches 94.2% in mAP@0.5 and achieves average increments by 2.1% . It improves the application and practicability of object detection technology in dense wildlife detection, which is encouraging and meaningful.
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Acknowledgments
This study was supported by the National Natural Science Foundation of China under Grant 62176217, the Innovation Team Funds of China West Normal University under Grant KCXTD2022-3, and the Fundamental Research Funds of China West Normal University under Grant 19B045.
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Pei, Y., Xu, L., Zheng, B. (2022). Improved YOLOv5 for Dense Wildlife Object Detection. In: Deng, W., et al. Biometric Recognition. CCBR 2022. Lecture Notes in Computer Science, vol 13628. Springer, Cham. https://doi.org/10.1007/978-3-031-20233-9_58
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DOI: https://doi.org/10.1007/978-3-031-20233-9_58
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