Wildlife detection and identification based on the improved YOLOv7

Zhifu Sun; Yongquan Zhang

doi:10.1117/12.3021527

19 February 2024 Wildlife detection and identification based on the improved YOLOv7

Zhifu Sun, Yongquan Zhang

Proceedings Volume 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023); 1306306 (2024) https://doi.org/10.1117/12.3021527
Event: Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 2023, Changchun, China

Abstract

To address the problems of low accuracy and poor robustness of wildlife object Detection, this paper proposes an improved wildlife detection algorithm based on YOLOv7(You Only Look Once v7). The proposed algorithm introduces Deformable ConvNets v2 (DCNv2) and Wise-IoU (WIoU) to improve the model feature extraction and learning ability. In the self-built wildlife data set, when the Intersection over Union (IoU) was 0.5, the proposed algorithm was in36wildlife categories, the mean Average Precision (mAP) increased by 1.2 percentage points over the original YOLOv7percentage points, precision increased by 4.1 percentage points, and recall increased by 2.2 percentage points. Experimental results show that the proposed improved YOLOv7 algorithm performance is better, more can meet the actual wildlife protection work of animal detection and identification accuracy requirements, contribute to the wildlife local survey work, and save a lot of related resource cost, to a certain extent, promote the wildlife protection work.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zhifu Sun and Yongquan Zhang "Wildlife detection and identification based on the improved YOLOv7", Proc. SPIE 13063, Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023), 1306306 (19 February 2024); https://doi.org/10.1117/12.3021527

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