Chen Luya, Zhu Jianping
College of Engineering Science and Technology, Shanghai Ocean University, Shanghai, 21306, China.
School of Engineering, Shanghai Ocean University, Shanghai, 21306, China.
Sci Rep. 2024 Mar 13;14(1):6133. doi: 10.1038/s41598-024-55051-3.
With the development of deep learning technology, researchers are increasingly paying attention to how to efficiently salvage surface garbage. Since the 1980s, the development of plastic products and economic growth has led to the accumulation of a large amount of garbage in rivers. Due to the large amount of garbage and the high risk of surface operations, the efficiency of manual garbage retrieval will be greatly reduced. Among existing methods, using YOLO algorithm to detect target objects is the most popular. Compared to traditional detection algorithms, YOLO algorithm not only has higher accuracy, but also is more lightweight. This article presents a lightweight YOLOv5 water surface garbage detection algorithm suitable for deployment on unmanned ships. This article has been validated on the Orca dataset, experimental results showed that the detection speed of the improved YOLOv5 increased by 4.3%, mAP value reached 84.9%, precision reached 88.7%, the parameter quantity only accounts for 12% of the original data. Compared with the original algorithm, the improved algorithm not only has higher accuracy, but also can be applied to more hardware devices due to its lighter weight.
随着深度学习技术的发展，研究人员越来越关注如何高效打捞水面垃圾。自20世纪80年代以来，塑料制品的发展和经济增长导致河流中堆积了大量垃圾。由于垃圾数量庞大且水面作业风险高，人工打捞垃圾的效率将大大降低。在现有方法中，使用YOLO算法检测目标物体最为流行。与传统检测算法相比，YOLO算法不仅具有更高的准确率，而且更加轻量级。本文提出了一种适用于无人船部署的轻量级YOLOv5水面垃圾检测算法。本文在Orca数据集上进行了验证，实验结果表明，改进后的YOLOv5检测速度提高了4.3%，mAP值达到84.9%，精度达到88.7%，参数量仅占原始数据的12%。与原算法相比，改进后的算法不仅具有更高的准确率，而且由于其更轻的重量，可以应用于更多的硬件设备。
