一种高精度优化Faster-RCNN变电站安全帽检测方法

A Detection Method for Safety Helmet in Substation Based on Improved High-precision Faster-RCNN

摘要:

针对传统变电站人工视频安全帽检测效率低和错误率高的问题，文中提出了一种优化的高精度Faster-RCNN安全帽检测方法。首先，以RPN为主干网络引入特征金字塔，增强检测算法浅层和深层信息表征能力；接着，引入K-Means++聚类算法优化Anchor参数，提升网络对小目标的检测能力；然后，以ROI Align代替RoI池化，消除量化误差和原始图片与特征图的映射偏差，从而提高检测精度；最后，采用开源安全帽数据集对改进的网络进行训练与测试，并将该模型与YOLOv3、RFBnet和传统Faster-RCNN进行了对比。实验结果表明：优化的Faster-RCNN相比RFBnet、YOLOv3和传统Faster-RCNN模型，将mAP值分别提高了6.81%、9.57%和5.09%，达到了92.43%；检测速度为18 frame/s，同时增强了变电站安全帽高精度识别能力。

Abstract:

Aiming at low efficiency and a high error rate of safety helmet detectionin substations by the traditional manual video detection method, a helmet detection method based on the improved high-precision Faster-RCNN is proposed. Firstly, the feature pyramid network module is introduced into the RPN backbone network, so that the representation