PAW-YOLOv7:河道微小漂浮物检测算法

PAW-YOLOv7:algorithm for detection of tiny floating objects in river channels

河道漂浮物检测对于船舶自动驾驶以及河道清理有着重大意义,但现有的方法在针对河道漂浮物目标尺寸小且互相遮挡、特征信息少时出现检测精度低等问题。为解决这些问题,本文基于YOLOv7,提出了一种改进模型PAWYOLOv7。首先,为了提高网络模型对小目标的特征表达能力,构建了小目标物体检测层,并将自注意力和卷积混合模块(ACmix)集成应用于新构建的小目标检测层;其次,为了减少复杂背景的干扰,采用全维动态卷积(ODConv)代替颈部的卷积模块,使网络具有捕获全局上下文信息能力;最后,将PConv(partial convolution)模块融入主干网络,替换部分标准卷积,同时采用WIoU(Wise-IoU)损失函数取代CIoU,实现网络模型计算量的降低,提高网络检测速度,同时增加对低质量锚框的聚焦能力,加快模型收敛速度。实验结果表明,PAW-YOLOv7算法在本文利用数据扩展技术改进的FloW-Img数据集上的检测精度达到89.7%,较原YOLOv7提升了9.8%,且检测速度达到54帧/秒(FPS),在自建的稀疏漂浮物数据集上的检测精度比YOLOv7提高了3.7%,能快速准确地检测河道微小漂浮物,同时也具有较好的实时检测性能。

Detection of floating debris in rivers is of great significance for ship autopilot and river cleaning,but the existing methods in targeting floating objects in the river with small target sizes and mutual occlusion,and less feature information lead to low detection accuracy.To address these problems,this paper proposes a small target object detection method called PAW-YOLOv7 based on YOLOv7.Firstly,in order to improve the feature expression ability of the small target network model,a small target object detection layer is constructed,and the self-attention and convolution hybrid module(ACmix)is integrated and applied to the newly constructed small target detection layer.Secondly,in order to reduce the interference of the complex background,the Omni-dimensional dynamic convolution(ODConv)is used instead of the convolution module in the neck,so as to give the network the ability to capture the global contextual information.Finally,the PConv(partial convolution)module is integrated into the backbone network to replace part of the standard convolution,while the WIoU(Wise-IoU)loss function is used to replace the CIoU.It achieves the reduction of network model computation,improves the network detection speed,and increases the focusing ability on the low-quality anchor frames,accelerating the convergence speed of the model.The experimental results show that the detection accuracy of the PAW-YOLOv7 algorithm on the FloW-Img dataset improved by the data extension technique in this paper reaches 89.7%,which is 9.8%higher than that of the original YOLOv7,the detection speed reaches 54 frames per second(FPS),and the detection accuracy on the self-built sparse floater dataset improves by 3.7%compared with that of YOLOv7.It is capable of detecting the tiny floating objects in the river channel quickly and accurately,and also has a better real-time detection performance.