改进 YOLO v7 算法下的监控水域环境人员识别研究

基于水域监控系统智能化的发展需求，提出了一种监控水域环境下人员识别算法。在水域场景数据采集、数据清洗与标记后，自主构建了一套监控水域场景下的人员类别数据集YZ-Water4,共8 092张图片和24 011个标签。基于目标检测算法YOLO v7的性能基础，针对水域场景特点，提出了适用于水域环境的目标检测算法YOLO-WA(you only look once-water area)。首先，使用更适合视觉任务的FReLU激活函数取代YOLO v7算法中激活函数；其次将注意力机制融合到算法网络骨架中，提升算法的特征提取能力；最后，选择SIOU损失函数替换YOLO v7算法中的CIOU损失函数以优化算法训练过程。实验结果表明，YOLO-WA与原算法相比，在水域人员类别数据集上识别精确率由82.3%提升到86.9%,召回率由92.0%提升到92.8%,平均精度从88.4%提高到90.6%,检测速度达到了85 fps,满足实时运行的精度与速度要求。

Research on the personnel recognition in monitored water area based on improved YOLO v7 algorithm

Based on the development demand of intelligent water area monitoring system, a personnel recognition algorithm for monitored water area is proposed. After data collection of the water area scene, data cleaning and labeling, a personnel category dataset YZ-Water4 under the monitored water area scene was independently constructed, with a total of 8 092 images and 24 011 tags. Based on the performance of the object detection algorithm YOLO v7 and the characteristics of the water area scene, object detection algorithm YOLO-WA (you only look once-water area) for water environment is proposed. First, the FReLU activation function which is proposed for visual tasks is used to replace the activation function in YOLO v7 algorithm. Secondly, the attention mechanism is integrated into algorithm to improve the feature extraction ability of the algorithm. Finally, SIOU loss function is chosen to replace CIOU loss function in YOLO v7 algorithm to optimize the training process. The experimental results show that compared with the original algorithm, YOLO-WA has increased the precision rate from 82. 3% to 86. 9%, recall rate from 92. 0% to 92. 8%, mean average precision from 88. 4% to 90. 6%, and the processing speed is 85 frame per second, meeting the accuracy and speed requirements of real-time run.