基于SOE-YOLO轻量化的水面目标检测算法

A water surface target detection algorithm based on SOE-YOLO lightweight network

针对复杂多变的水面环境,小目标检测存在漏检、错检且检测平台计算资源有限的问题,提出了基于YOLOv8的轻量化水面目标检测算法SOE-YOLO。首先在Neck部分使用包含GSConv的Slim-Neck设计范式对模型进行轻量化改进;其次通过使用轻量型卷积(ODConv)模块重新构建Backbone部分,以减少参数量从而提高网络的检测速度;最后引入多尺度注意力机制(EMA)增强网络多尺度特征提取能力,提高小目标检测能力。在WSODD测试集中的实验结果表明,SOE-YOLO模型参数量和计算量分别为2.8 M和6.6 GFLOPs,与原模型相比分别减少12.5%和18.6%,同时mAP@%0.5和mAP@0.5-0.95分别达到79.9%和47.2%,与原模型相比分别提高2.4%和1.6%,且漏检率下降明显,优于当前流行的目标检测算法。FPS达到了64.25,满足水面目标检测实时性的要求。在实现轻量化的同时具有更好的检测性能,满足了在计算资源受限环境下的部署需求。

A lightweight water surface object detection algorithm SOE-YOLO based on YOLOv8 was proposed to address the issues of missed and false detections in complex and ever-changing water surface environments,as well as limited computing resources on the detection platform.Firstly,the Slim-Neck paradigm containing GSConv was employed to improve the weight of the model in the Neck part.Secondly,the Backbone section was reconstructed using a lightweight convolutional ODConv(omni-dimensional dynamic convolution)module,thereby reducing the number of parameters to improve the detection speed of the network.Finally,the multi-scale attention mechanism EMA(effective multi-scale attention)was introduced to enhance the network’s capability in extracting multi-scale features,thereby enhancing the small target detection accuracy.The experimental results on the WSODD(water surface object detection)test set demonstrated that the parameter and computational quantities of the SOE-YOLO model were 2.8 M and 6.6 GFLOPs,respectively,which were reduced by 12.5%and 18.6%compared to the original model.At the same time,mAP@%0.5 and mAP@0.5-.95 reached 79.9%and 47.2%,respectively,which were 2.4%and 1.6%higher than the original model,and the missed detection rate decreased significantly,outperforming the current popular object detection algorithms.The FPS reached 64.25,meeting the requirements of real-time detection of surface targets.It could achieve better detection performance,while achieving lightweight,meeting deployment requirements in computing-resource-constrained environments.