To ensure the safe operation of highway traffic lines, given the imperfect feature extraction of existing road pit defect detection models and the practicability of detection equipment, this paper proposes a lightweight target detection algorithm with enhanced feature extraction based on the YOLO (You Only Look Once) algorithm. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion to enhance the feature extraction ability, and Varifocal Loss is used to optimize the sample imbalance problem, which improves the accuracy of road defect target detection. In the evaluation test of the model in the constructed PCD1 (Pavement Check Dataset) dataset, the mAP@.5 (mean Average Precision when IoU = 0.5) of the BV-YOLOv5S (BiFPN Varifocal Loss-YOLOv5S) model increased by 4.1%, 3%, and 0.9%, respectively, compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S (BiFPN-YOLOv5S; BV-YOLOv5S does not use the Improved Focal Loss function) models. Through the analysis and comparison of experimental results, it is proved that the proposed BV-YOLOv5S network model performs better and is more reliable in the detection of pavement defects and can meet the needs of road safety detection projects with high real-time and flexibility requirements.
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.
The use of drones to inspect transmission lines is an important task for the energy maintenance department to ensure the stability and safety of power transmission. However, the current electric power inspection is inseparable from the participation of artificial vision. It is necessary to establish an automatic visual recognition technology with high reliability, high flexibility, and low embedded cost. This paper develops an improved YOLOv5S deep-learning-based transmission line disaster prevention safety detection model, called Model E. Compared to the original network, we use the Ghost convolution operation in the Model E network to improve the redundant computation caused by the conventional convolution operation. The BiFPN network structure is adopted to enhance the feature extraction ability of the original PANet network for unsafe objects in the transmission line image. This occurs in the process of Model E transmission line disaster prevention safety detection model learning. Equalized Focal Loss (EFL) is used to improve the Model E sample imbalance problem processing mechanism. The Model E proposed in this paper is 6.9%, 1.7%, 1.7%, and 2.9% higher than the current lightweight mainstream algorithms YOLOv3-Tiny and YOLOv5S, Model C (based on the original YOLOv5S network, the BiFPN structure in the Model E network part is improved), and Model D network (in the Backbone layer, four conventional convolutions are improved as Ghost convolution operations, and the rest of the structure is the same as the Model E network) in mAP@.5 evaluation index. Meanwhile, the size of the model is only 79.5%, 97.7%, 84.9%, and 93.8% of the above algorithm model. The experimental results show that the Model E transmission line disaster prevention and safety detection model proposed in this paper shows stronger competitiveness and advancement, with high reliability, flexibility, and fast detection ability, and can be applied to cost, reliability, and efficiency in order to have a higher standard of practical engineering needs.
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