基于SPMYOLOv3的水面垃圾目标检测

Target Detection of Water Surface Garbage Based on SPMYOLOv3

为解决水面垃圾检测中存在目标形状尺度差异大,难以区分背景以及目标偏小的问题,本文提出了一种SPMYOLOv3目标检测算法来实现对水面垃圾的检测.首先,对收集到的水面垃圾数据集进行标注,使用改进的K-means算法对数据集重新聚类,得到与数据集更匹配的先验框.其次,在YOLOv3的主干网络后添加SE-PPM模块,加强目标的特征信息,保证目标尺度不变且保留全局信息.再使用多向金字塔网络对不同尺度的特征图进行融合,获得携带更加丰富的上下文信息的特征图.最后使用在损失函数中使用focal loss计算负样本的置信度损失,抑制了YOLOv3中正负样本不均衡问题.改进后的算法在水面垃圾数据集上的实验结果表明,相比于原YOLOv3算法检测精度提升了3.96%.

In water surface garbage detection, large differences occur in target shape and scale, and it is difficult to distinguish the background and the small target. Thus, this study proposes the SPMYOLOv3 detection algorithm to identify surface garbage. Firstly, massive surface garbage datasets are collected and annotated, and an improved K-means clustering method is applied to generate the priori boxes that better match the datasets. Secondly, the SE-PPM module is added after the backbone network of YOLOv3 for strengthening the feature information of the target, ensuring that the target scale remains unchanged and the global information is preserved. The multidirectional FPN is then applied to fuse the feature maps of different scales so that the feature maps after fusion contain richer context information. Finally, the Focal Loss is adopted to compute the confidence loss of negative samples, which alleviates the imbalance of positive and negative samples in YOLOv3. The modified algorithm is tested on the water surface garbage dataset, and the results show that the accuracy of the modified algorithm is 3.96% higher than that of the original YOLOv3 algorithm.