Lightweight YOLOv8-ALTE algorithm for bridge crack disease detection

To address low efficiency, poor detection accuracy, and high missed detection rates in bridge crack detection under complex conditions, a lightweight algorithm named YOLOv8-ALTE based on an improved YOLOv8 was proposed. On the basis of the YOLOv8-N model, its C2f module was integrated with a lightweight convolutional module, ALConv, capable of perceiving multi-scale feature information, to enrich crack-related information in the extracted feature maps. A triplet attention was embedded into the shallow layers of the network's feature extraction module to enhance the model's accuracy of locating and identifying bridge cracks. A lightweight decoupled head, designed with parameter-sharing, replaced the original decoupled head, effectively reducing the computational complexity of the model. Additionally, a multi-parameter distance intersection over union loss was introduced to replace the original regression loss, enabling higher efficiency and accuracy in bounding box regression. A bridge crack detection dataset with various complex background conditions was constructed through manual annotation. Multiple data augmentation techniques were employed to organize and expand the dataset. Precision, recall, average precision (AP50 and AP50-95), and floating point operations (FLOPs) were adopted as quantitative evaluation metrics. The model was evaluated through comparison, module integration, attention mechanism incorporation, and ablation experiments. Experimental results demonstrate that YOLOv8-ALTE achieves a precision of 93.9%, a recall of 83.5%, an AP50 of 89.0%, an AP50-95 of 73.8%, and a FLOPs of 8.0. The comprehensive performance of YOLOv8-ALTE outperforms the original YOLOv8-N and other compared models, proving the superiority of the proposed algorithm. YOLOv8-ALTE enables efficient and accurate detection of bridge cracks while improving computational efficiency.