.png){kind=icon}
返回
Development of a Lightweight Floating Object Detection Algorithm
.png){kind=icon}
OA
AI
DOI：10.3390/w16111633.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
YOLOv5 is currently one of the mainstream algorithms for object detection. In this paper, we propose the FRL-YOLO model specifically for river floating object detection. The algorithm integrates the Fasternet block into the C3 module, conducting convolutions only on a subset of input channels to reduce computational load. Simultaneously, it effectively captures spatial features, incorporates reparameterization techniques into the feature extraction network, and introduces the RepConv design to enhance model training efficiency. To further optimize network performance, the ACON-C activation function is employed. Finally, by employing a structured non-destructive pruning approach, redundant channels in the model are trimmed, significantly reducing the model's volume. Experimental results indicate that the algorithm achieves an average precision value (mAP) of 79.3%, a 0.4% improvement compared to yolov5s. The detection speed on the NVIDIA GeForce RTX 4070 graphics card reaches 623.5 fps/s, a 22.8% increase over yolov5s. The improved model is compressed to a volume of 2 MB, representing only 14.7% of yolov5s.
Keyword:
YOLOv5 algorithm
floating object detection
lightweight model
期刊
W
IF:
3
论文数:
3.2W
被引数:
7.4W
机构
暂无机构信息
引用论文
Thermal comfort, perceived air quality, and cognitive performance when personally controlled air movement is used by tropically acclimatized persons当热带适应的人使用个人控制的空气运动时，热舒适性，感知的空气质量和认知表现.png)
Indoor Air
OAAI
Indoor AirIF0
OAAI分享收藏Application of EfficientNet and YOLOv5 Model in Submarine Pipeline Inspection and a New Decision-Making SystemWATEROAAI
WATERIF3
OAAI分享收藏
查看更多内容