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  • 1College of Mechanical and Electrical Engineering, Hunan Agricultural University, Changsha 410128, China.

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Summary
This summary is machine-generated.

An improved YOLOv5s network enhances forest wildlife detection accuracy using advanced feature extraction and novel loss functions. This algorithm significantly boosts detection performance in complex environments, aiding conservation efforts.

Keywords:
Swin TransformerYOLOv5sdata set annotation and augmentationdetection algorithm improvementsforest wildlifenetwork convergence

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Ecology

Background:

  • Forest wildlife monitoring is crucial for conservation but faces challenges like complex environments and image quality.
  • Existing detection algorithms struggle with low contrast, occlusion, and overlapping targets in trap camera imagery.

Purpose of the Study:

  • To develop an improved YOLOv5s network model for accurate forest wildlife detection.
  • To enhance the feature extraction capabilities and detection accuracy in challenging forest conditions.

Main Methods:

  • Utilized a dataset from Hunan Hupingshan National Nature Reserve with data augmentation.
  • Incorporated a weighted channel stitching method with channel attention and Swin Transformer for enhanced feature extraction.
  • Implemented a new loss function (DIOU_Loss) and adaptive class suppression loss (L_BCE) to improve convergence and reduce false detections.

Main Results:

  • The improved algorithm achieved a mean average precision (mAP) of 89.4%, an increase of 16.8% over the original YOLOv5s.
  • Demonstrated superior performance compared to YOLOv5s, YOLOv3, RetinaNet, and Faster-RCNN.
  • Effectively addressed challenges of low contrast, occlusion, and target overlap in forest wildlife images.

Conclusions:

  • The proposed algorithm significantly improves forest wildlife detection accuracy in complex environments.
  • The enhancements provide practical solutions for effective wildlife protection and data acquisition.
  • This work offers a robust tool for ecological research and conservation initiatives.