基于改进全卷积一阶检测器的桥梁裂缝定位算法

为解决桥梁裂缝检测时定位速度慢的问题，提出一种基于全卷积一阶（FCOS）检测器的裂缝定位改进算法。本算法采用FCOS网络模型，利用轻量级骨干网络Efficientnet提取裂缝图像特征，作为改进措施，引入加权双向特征金字塔网络（BiFPN）融合裂缝图像不同尺度的特征，从而进一步增强骨干网络的视觉特征提取效果。在自制数据...

Bridge Crack Location Algorithm Based on Improved Fully Convolutional One-stage Detector

In order to solve the problem of slow locating speed in the process of bridge crack detection， a crack location algorithm based on fully convolutional one-stage(FCOS) detector is proposed. The algorithm uses the FCOS network model framework， uses the lightweight backbone network efficientnet to extract the characteristics of the crack image. And bi-directional feature pyramid network is introduced to fuse the characteristics of different scales of the crack image， thereby enhancing the visual feature extraction effect of the backbone network. Experiments on a self-made data set show that the algorithm can quickly locate bridge cracks. Compared with FCOS， the detection speed is increased by 10.4 frames per second while ensuring that the average accuracy is not attenuated. Compared with Faster-RCNN， Yolo4， Efficientdet， detection speed and average accuracy have obvious advantages.