面向嵌入式平台的安全帽实时检测方法

针对当前基于深度学习的安全帽检测方法因结构复杂和计算量大,难以在嵌入端实现实时检测的问题,提出一种适用于嵌入式平台的轻量化安全帽实时检测方法.该方法以Tiny-YOLOv3检测网络为基础,通过改进特征提取网络和多尺度预测优化网络结构,引入空间金字塔池化模块丰富特征图的多尺度信息,采用K-means聚类算法确定适合安全帽...

Real-Time Hardhats Detection Method for Embedded Platform

Aiming at the problem that current deep learning based hardhat detection methods are difficult to achieve real-time detection at the embedded devices due to complex structure and large amount of calculation, a light-weight hardhat detection method is proposed for embedded platform. Based on Tiny-YOLOv3, the network structure is optimized by improving the feature extraction network and multi-scale prediction. The spatial pyramid pooling module is introduced to enrich the multi-scale information of feature map. K-means clustering algorithm is used to determine the anchor frame suitable for hardhats detection. The bounding box regression loss function CIoU is introduced to improve detection accuracy. Experimental results show that, with the input size of 608×608, the average accuracy, recall rate and F1 value of the proposed method are 87.50%, 84% and 83%, which is 11.27, 11 and 7 percentage points higher than that of Tiny-YOLOv3 detection method. Meanwhile, the proposed method can achieve a real-time detection speed of 20.58 frames per second on the embedded platform NVIDIA Jetson Nano, which can meet the requirement of real-time detection when running on the embedded platform. In addition, the proposed method has good adaptability and generalization in complex construction environment such as poor light, small targets and dense targets.