Application Research of Bridge Damage Detection Based on the Improved Lightweight Convolutional Neural Network Model

Abstract

To ensure the safety and rational use of bridge traffic lines, the existing bridge structural damage detection models are not perfect for feature extraction and have difficulty meeting the practicability of detection equipment. Based on the YOLO (You Only Look Once) algorithm, this paper proposes a lightweight target detection algorithm with enhanced feature extraction of bridge structural damage. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion, which enhances the ability to extract damage features of bridge structures, and uses EFL (Equalized Focal Loss) to optimize the sample imbalance processing mechanism, which improves the accuracy of bridge structure damage target detection. The evaluation test of the model has been carried out in the constructed BDD (Bridge Damage Dataset) dataset. Compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S models, the mAP@.5 of the BE-YOLOv5S model increased by 45.1%, 2%, and 1.6% respectively. The analysis and comparison of the experimental results prove that the BE-YOLOv5S network model proposed in this paper has a better performance and a more reliable performance in the detection of bridge structural damage. It can meet the needs of bridge structure damage detection engineering with high requirements for real-time and flexibility.

Automated optical inspection of FAST’s reflector surface using drones and computer vision

“…Next, we harnessed deep-learning techniques to detect defects using the captured videos [1,2]. Computer-vision technology based on deep learning has been widely used for defect detection in concrete and steel structures, such as pavement [3,4,5] and bridges [6,7]. Other studies [8,9] have detected insulator faults using aerial images of high-voltage transmission lines based on improved YOLOv3 models [10].…”

Infrared Target Detection Based on Interval Sampling Weighting and 3D Attention Head in Complex Scenario

“…Wen et al [23] used lightweight PConv to replace the original convolutional layer to reduce parameters and introduced coordinate attention in the backbone to enhance the ability to position the target. Du et al [24] introduced BIFPN to improve the fusion network so that it could capture and utilize multiscale features more effectively, thus enhancing the performance of the object detector. Wei et al [25] proposed the use of a radius-aware loss function to consider the radius between the prediction result and the target to better evaluate the accuracy of the prediction.…”