HIGHLIGHTS
- What: To prove the effectiveness of the algorithm proposed in this paper for mussel object detection, in this section, it is experimentally compared with the YOLOv5s algorithm and the Faster R-CNN algorithm on the self-made mussel dataset. In this paper, the YOLOv5s network architecture and the process of vehicle detection are deeply studied.
- Who: Zhaopeng Dong from the (UNIVERSITY) have published the research: Vehicle Target Detection Using the ImprovedYOLOv5s Algorithm, in the Journal: Electronics 2024, 4672 of /2024/
- How: This involved randomly selecting To mitigate the risk of overfittingtechniques during training . . .
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