We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
弱光条件下猪舍清洗目标检测.
- Authors
李颀; 李煜哲
- Abstract
The cleaning of piggery without manual intervention can effectively prevent pigs from contracting diseases. Aiming at the problems of weak light, inconspicuous features, and mutual occlusion in the piggery scene, the low detection accuracy of cleaning targets, an improved YOLOv5 algorithm was proposed. In the preconditioning stage, the Retinex algorithm based on bilateral filtering was used to improve the detection ability of the piggery cleaning target under low light conditions; In the cleaning target detection process, the CBAM (convolutional block attention module) attention mechanism was introduced into the backbone of the YOLOv5 network to enable the network to learn more effective feature information of the cleaning target in the piggery; The frame regression loss function in the network was improved, and the DIoU-NMS algorithm was used to filter out the candidate frame of the cleaning target in the piggery, which improved the detection accuracy of the cleaning target in the case of partial occlusion. The experimental results showed that the improved YOLOv5 target detection algorithm improved the accuracy rate by 7.3 percentage point, the recall rate by 7.6 percentage point, and the average accuracy by 7.1 percentage point compared with the benchmark algorithm on the test set of piggery cleaning targets. The robustness was higher in the case of target occlusion. The result provided a basis for the research and development of animal husbandry intelligent cleaning equipment.
- Publication
Acta Agriculturae Zhejiangensis, 2023, Vol 35, Issue 9, p2240
- ISSN
1004-1524
- Publication type
Article
- DOI
10.3969/j.issn.1004-1524.20221447