Although the Faster Region-based Convolutional Neural Network(Faster R-CNN)model has obvious advantages in defect recognition,it still cannot overcome challenging problems,such as time-consuming,small targets,irregula...Although the Faster Region-based Convolutional Neural Network(Faster R-CNN)model has obvious advantages in defect recognition,it still cannot overcome challenging problems,such as time-consuming,small targets,irregular shapes,and strong noise interference in bridge defect detection.To deal with these issues,this paper proposes a novel Multi-scale Feature Fusion(MFF)model for bridge appearance disease detection.First,the Faster R-CNN model adopts Region Of Interest(ROl)pooling,which omits the edge information of the target area,resulting in some missed detections and inaccuracies in both detecting and localizing bridge defects.Therefore,this paper proposes an MFF based on regional feature Aggregation(MFF-A),which reduces the missed detection rate of bridge defect detection and improves the positioning accuracy of the target area.Second,the Faster R-CNN model is insensitive to small targets,irregular shapes,and strong noises in bridge defect detection,which results in a long training time and low recognition accuracy.Accordingly,a novel Lightweight MFF(namely MFF-L)model for bridge appearance defect detection using a lightweight network EfficientNetV2 and a feature pyramid network is proposed,which fuses multi-scale features to shorten the training speed and improve recognition accuracy.Finally,the effectiveness of the proposed method is evaluated on the bridge disease dataset and public computational fluid dynamic dataset.展开更多
The complex operating environment in substations, with different safety distances for live equipment, is a typical high-risk working area, and it is crucial to accurately identify the type of live equipment during aut...The complex operating environment in substations, with different safety distances for live equipment, is a typical high-risk working area, and it is crucial to accurately identify the type of live equipment during automated operations. This paper investigates the detection of live equipment under complex backgrounds and noise disturbances, designs a method for expanding lightweight disturbance data by fitting Gaussian stretched positional information with recurrent neural networks and iterative optimization, and proposes an intelligent detection method for MD-Yolov7 substation environmental targets based on fused multilayer feature fusion (MLFF) and detection transformer (DETR). Subsequently, to verify the performance of the proposed method, an experimental test platform was built to carry out performance validation experiments. The results show that the proposed method has significantly improved the performance of the detection accuracy of live devices compared to the pairwise comparison algorithm, with an average mean accuracy (mAP) of 99.2%, which verifies the feasibility and accuracy of the proposed method and has a high application value.展开更多
Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely dis...Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.展开更多
基金This work was supported by the National Natural Science Foundation of China(No.61976247)the Major R&D Programs of China(No.2019YFB-1310400).
文摘Although the Faster Region-based Convolutional Neural Network(Faster R-CNN)model has obvious advantages in defect recognition,it still cannot overcome challenging problems,such as time-consuming,small targets,irregular shapes,and strong noise interference in bridge defect detection.To deal with these issues,this paper proposes a novel Multi-scale Feature Fusion(MFF)model for bridge appearance disease detection.First,the Faster R-CNN model adopts Region Of Interest(ROl)pooling,which omits the edge information of the target area,resulting in some missed detections and inaccuracies in both detecting and localizing bridge defects.Therefore,this paper proposes an MFF based on regional feature Aggregation(MFF-A),which reduces the missed detection rate of bridge defect detection and improves the positioning accuracy of the target area.Second,the Faster R-CNN model is insensitive to small targets,irregular shapes,and strong noises in bridge defect detection,which results in a long training time and low recognition accuracy.Accordingly,a novel Lightweight MFF(namely MFF-L)model for bridge appearance defect detection using a lightweight network EfficientNetV2 and a feature pyramid network is proposed,which fuses multi-scale features to shorten the training speed and improve recognition accuracy.Finally,the effectiveness of the proposed method is evaluated on the bridge disease dataset and public computational fluid dynamic dataset.
文摘The complex operating environment in substations, with different safety distances for live equipment, is a typical high-risk working area, and it is crucial to accurately identify the type of live equipment during automated operations. This paper investigates the detection of live equipment under complex backgrounds and noise disturbances, designs a method for expanding lightweight disturbance data by fitting Gaussian stretched positional information with recurrent neural networks and iterative optimization, and proposes an intelligent detection method for MD-Yolov7 substation environmental targets based on fused multilayer feature fusion (MLFF) and detection transformer (DETR). Subsequently, to verify the performance of the proposed method, an experimental test platform was built to carry out performance validation experiments. The results show that the proposed method has significantly improved the performance of the detection accuracy of live devices compared to the pairwise comparison algorithm, with an average mean accuracy (mAP) of 99.2%, which verifies the feasibility and accuracy of the proposed method and has a high application value.
基金The authors are grateful to the Deanship of Scientific Research,King Saud University,Riyadh,Saudi Arabia,for funding this work through the Vice Deanship of Scientific Research Chairs:Research Chair of Pervasive and Mobile Computing.
文摘Real-time pedestrian detection is an important task for unmanned driving systems and video surveillance.The existing pedestrian detection methods often work at low speed and also fail to detect smaller and densely distributed pedestrians by losing some of their detection accuracy in such cases.Therefore,the proposed algorithm YOLOv2(“YOU ONLY LOOK ONCE Version 2”)-based pedestrian detection(referred to as YOLOv2PD)would be more suitable for detecting smaller and densely distributed pedestrians in real-time complex road scenes.The proposed YOLOv2PD algorithm adopts a Multi-layer Feature Fusion(MLFF)strategy,which helps to improve the model’s feature extraction ability.In addition,one repeated convolution layer is removed from the final layer,which in turn reduces the computational complexity without losing any detection accuracy.The proposed algorithm applies the K-means clustering method on the Pascal Voc-2007+2012 pedestrian dataset before training to find the optimal anchor boxes.Both the proposed network structure and the loss function are improved to make the model more accurate and faster while detecting smaller pedestrians.Experimental results show that,at 544×544 image resolution,the proposed model achieves 80.7%average precision(AP),which is 2.1%higher than the YOLOv2 Model on the Pascal Voc-2007+2012 pedestrian dataset.Besides,based on the experimental results,the proposed model YOLOv2PD achieves a good trade-off balance between detection accuracy and real-time speed when evaluated on INRIA and Caltech test pedestrian datasets and achieves state-of-the-art detection results.
