Research on road crack detection technology based on Deeplabv3+

To provide more reliable data support for road maintenance, this paper proposes a DeepLabv3+ semantic segmentation algorithm that integrates dense and attention mechanisms. Due to the indistinct boundaries of weak cracks, convolutional kernels are difficult to match with crack images, resulting in the loss of crack information in some segmentation results. Therefore, a dense connection mechanism is introduced into the baseline network. To address segmentation errors caused by interference such as shadows and zebra crossings in crack images, two attention mechanisms are further integrated. Experimental results show that the segmentation performance is significantly improved after introducing the dense connection mechanism, with the mean Intersection over Union (MIoU) reaching 80.2%, a 4.7% improvement over the baseline network, while the segmentation speed is also superior. The integration of dense connections and attention mechanisms effectively improves the segmentation performance of the network, with an MIoU of 83.9%, which is 8.4% higher than traditional methods, and a pixel accuracy (PA) of 98.8%, which is 6.7% higher than traditional methods. Therefore, this method outperforms traditional methods in the segmentation performance of road crack images and also demonstrates superior segmentation speed.