Duck Down Recognition and Classification Based on YOLOv8 Improved by GSconv,CBAM and BiFPN
ID：111
Submission ID：7 View Protection：ATTENDEE
Updated Time：2024-10-23 10:00:24 Hits：26
Poster Presentation
Start Time：Pending （Asia/Shanghai）
Duration：Pending
Session：[No Session] » [No Session Block]
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Abstract
The composition of duck down is complex and difficult to separate, so at present, in the process of composition detection of duck down, the composition of duck down are recognized by the method of artificial separation, which is inefficient and subjective. In this paper, a duck down recognition method based on the improved YOLOv8 algorithm is proposed, which realizes image enhancement through image preprocessing and reduces the influence of complex background. By improving the YOLOv8 algorithm, the accurate identification of duck down was realized. The lightweight modules GSconv and VoV-GSCSP are used to increase the speed of model detection and improve the ability to detect multiple targets. Add the CBAM module to improve the feature extraction ability and detection accuracy, and improve the model's ability to detect small targets. The experimental shows that the average accuracy of the improved model for the identification of duck down (MAP50 and MAP50-95) is 99.1% and 68.1%, respectively. By comparing the mainstream target detection networks, it is demonstrated that the improved YOLOv8 has the highest detection accuracy and the fastest detection speed, which makes it practical and reliable for detection of down and similar small and multi-target targets in practical applications.
Keywords
duck down, image processing, deep learning, small objectives, multi-objective, complex backgrounds
Speaker
DaotongZhang
Anhui University of Science and TechnologySubmission Author
All comments
15th August 2024 31st August 2024- Manuscript Submission
15th September 2024 - Acceptance Notification
1st October 2024 - Camera Ready Submission
1st October 2024 – Early Bird Registration
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