面向密集型钢筋计数的GCA-MobilenetV2-YOLOv4算法

为提高建筑工地的钢筋计数效率，围绕施工单位硬件设备算力不足，钢筋图像物体密集遮挡严重的情况，提出一种改进的轻量化YOLOv4算法。提出GCA-MobilenetV2轻量级网络替换CSPDarknet53,作为YOLOv4算法的主干特征网络。针对钢筋图像密集，物体间遮挡严重的情况，提出融合通道注意力机制的attention-CSP-PANet结构。针对深层网络SPP结构参数量大，模型训练时梯度消失的问题，提出DepthLite-SPP结构，增强深层网络的感受野，提高算法的检测速度。针对一阶段回归的算法正负样本失衡问题，设计CIOU-Focal损失函数。实验证明，在自建钢筋数据集中检测精度为98.78%,对比原算法精度提升了3.36%,检测速度FPS提升了7.6,参数量仅为原算法的1/3。

GCA-MobilenetV2-YOLOv4 algorithm for intensive rebar counting

To improve the counting efficiency of steel bars in construction sites, an improved lightweight YOLOv4 algorithm is proposed based on the insufficient computing power of hardware equipment in construction units and the dense occlusion of steel bar image objects. GCA-MobilenetV2 lightweight network is proposed to replace CSPDarknet53 as the main feature network of YOLOv4 algorithm. Aiming at the situation of dense steel bar images and serious occlusion between objects, attention-CSP-PANet structure integrating channel attention mechanism is proposed. Aiming at the large number of SPP structure parameters in deep network, DepthLite-SPP structure is proposed to enhance the receptive field of deep network and improve the detection speed of the algorithm. In view of the imbalance between positive and negative samples of the one-stage regression algorithm, CIOU-Focal loss function is designed. The experimental results show that the detection accuracy of the steel bar data set is 98.78%, which is 3.36% higher than that of the original algorithm. The detection speed FPS is 7.6, and the number of parameters is only 1/3 of the original algorithm.