Resumen
This study proposes a GC-YOLOv5s crack-detection network of UAVs to work out several issues, such as the low efficiency, low detection accuracy caused by shadows, occlusions and low contrast, and influences due to road noise in the classic crack-detection methods in the complicated traffic routes. A Focal-GIOU loss function with a focal loss has been introduced in this proposed algorithm, which is applied to address the issue of the imbalance of difficult and easy samples in crack images. Meanwhile, the original localization loss function CIOU is replaced by a GIOU loss function that is more suitable for irregular target (crack) detection. In order to improve the ability of the modified model of representing the features, a Transposed Convolution layer is simultaneously added in place of the original model?s upsampling layer. According to the advantage of computing resources of the Ghost module, the C3Ghost module is applied to decrease the amount of network parameters while maintaining adequate feature representation. Additionally, a lightweight module, CSPCM, is designed with the Conmix module and the Ghost concept, which successfully reduces the model parameters and zooms out the volume. At the same time, this modified module can have enough detection accuracy, and it can satisfy the requirements of UAV detection of small models and rapidity. In order to prove the model?s performance, this study has established a new UAV road-crack-detection dataset (named the UMSC), and has conducted extensive trials. To sum up, the precision of GC-YOLOv5s has increased by 8.2%, 2.8%, and 3.1%, respectively, and has reduced the model parameters by 16.2% in comparison to YOLOv5s. Furthermore, it outperforms previous YOLO comparison models in Precision, Recall, mAP_0.5, mAP_0.5:0.95, and Params.