YOLOv5 Wildlife Detection Based on Involution Operator
HE Pengfei
WANG Feifei
SUN Caihui
NIE Rong
LIU Zhihang
Abstract：Wildlife is an important part of the natural environment,and its conservation is of great importance to human devel-opment.Nowadays,using infrared cameras with deep learning algorithms to monitor wildlife provides an effective way for biological conservation.In this paper,an infrared wildlife image detection algorithm based on YOLOv5 is designed.This paper introduces the involution operator with feature splicing operation in the neck network part of YOLOv5.The original concat splicing operation of the neck network is improved.Its weighting operation is applied to different feature layers according to the importance of the features,giving higher weights to the important feature layers and making the network focus more on the key information.The improved algo-rithm in this paper has substantial improvement in all metrics compared with the original algorithm,providing a more effective meth-od for wildlife detection.
Keywords：wildlife detectionYOLOv5involution operatorfeature fusionfeature weighting
Publication Date：2025-03-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 701-707 )