基于深度学习的水面漂浮物识别算法设计

针对小型水域漂浮物识别困难问题，提出一种基于深度学习的目标识别方法，采用改进的YOLOv5s目标识别算法识别水面漂浮物．首先，根据水面漂浮物形状的特点，采用改进K-means算法，对先验框重新聚类，其次加入SE注意力机制模块，然后将α-IOU应用于YOLOv5s网络上．实验结果表明，对比标准的YOLOv5s算法，改进的YOLOv5s算法在查准率和平均精度均值方面分别提升了2%和4%，验证了算法的有效性，该方法能克服水面环境的影响，有效识别水面的漂浮物．

Design of Water Surface Floating Object Recognition Algorithm based on Deep Learning

For the difficult problem of small water floating object recognition, a deep learning-based target recognition method is improved. In this paper, an improved YOLOv5s target recognition algorithm is used to recognize water floating objects. First, according to the characteristics of the shape of floating objects on the water surface, the improved K-means algorithm is used to re-cluster the anchor boxes, then, Squeeze-and-Excitation networks module is added and then α-IOU is applied to the YOLOv5s network. The experimental results show that, compared with the standard YOLOv5s algorithm, the precision and average precision of the improved YOLOv5s algorithm are increased by 2% and 4% respectively, which verifies the effectiveness of the algorithm. This method can overcome the influence of water surface environment and effectively identify floating objects on the water surface.