A lightweight algorithm for wildlife detection in outdoor environments based on you only look once version 8 network

Abstract

To address the challenges posed by outdoor environments, such as obstructions, background noise, and the limited computational resources of detection devices that hinder accurate wildlife detection, we propose a lightweight wildlife detection algorithm named Wildlife-You Only Look Once (WL-YOLO). First, the High-level Screening-feature Pyramid Networks (HS-FPN) is introduced to enhance the processing of multi-scale wildlife features. This architecture captures comprehensive feature information while significantly reducing computational complexity. Next, the Depthwise Adaptive Spatial Attention (DASA) module is designed to improve the adaptability of the model to various environmental backgrounds, effectively addressing challenges such as object occlusion and complex backgrounds in wildlife images. Additionally, the Scylla Intersection over Union (SIoU) loss is utilized to optimize detection accuracy. Finally, a pruning method based on layer-adaptive magnitude-based pruning (LAMP) is applied to the model to trim redundant parameters. Experimental results indicate that WL-YOLO achieves comparable detection accuracy to the original YOLO model (version 8) while reducing parameters by 66.7% and computational load by 57.5%. It processes images at a speed of 344 frames per second. Furthermore, detection results indicate that WL-YOLO performs better in handling occlusions and complex environments. Compared to mainstream object detection algorithms, WL-YOLO achieves a favorable balance among detection accuracy, inference speed, and model complexity. The research suggests that WL-YOLO is better applicable in complex outdoor environments, offering a new pathway for research on wildlife diversity. The code will be available on GitHub (https://github.com/xust-9527/WL-YOLO).