ENGD-BiFPN: a remote sensing object detection model based on grouped deformable convolution for power transmission towers

Zha, Wenting; Hu, Longwei; Sun, Yalu; Li, Yalong

doi:10.1007/s11042-023-15584-7

Cited by 6 publications

(2 citation statements)

References 32 publications

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“…The BiFPN is proposed as a novel network structure for multiscale feature fusion. Compared to traditional unidirectional FPNs, the BiFPN improves fusion accuracy and efficiency by utilizing bidirectional connections and feature node fusion in the Feature Pyramid Network, solving the problem of unidirectional FPNs' inability to fully utilize feature information at different scales [45][46][47]. Therefore, in this study, the original FPN and PANet structures in YOLOv5 are improved to the BiFPN network to achieve more efficient multiscale feature fusion.…”

Section: Multi-scale Feature Fusionmentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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Flat-rolled steel sheets are one of the major products of the metal industry. Strip steel’s production quality is crucial for the economic and safety aspects of humanity. Addressing the challenges of identifying the surface defects of strip steel in real production environments and low detection efficiency, this study presents an approach for strip defect detection based on YOLOv5s, termed SDD-YOLO. Initially, this study designs the Convolution-GhostNet Hybrid module (CGH) and Multi-Convolution Feature Fusion block (MCFF), effectively reducing computational complexity and enhancing feature extraction efficiency. Subsequently, CARAFE is employed to replace bilinear interpolation upsampling to improve image feature utilization; finally, the Bidirectional Feature Pyramid Network (BiFPN) is introduced to enhance the model’s adaptability to targets of different scales. Experimental results demonstrate that, compared to the baseline YOLOv5s, this method achieves a 6.3% increase in mAP50, reaching 76.1% on the Northeastern University Surface Defect Database for Detection (NEU-DET), with parameters and FLOPs of only 3.4MB and 6.4G, respectively, and FPS reaching 121, effectively identifying six types of defects such as Crazing and Inclusion. Furthermore, under the conditions of strong exposure, insufficient brightness, and the addition of Gaussian noise, the model’s mAP50 still exceeds 70%, demonstrating the model’s strong robustness. In conclusion, the proposed SDD-YOLO in this study features high accuracy, efficiency, and lightweight characteristics, making it applicable in actual production to enhance strip steel production quality and efficiency.

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Section: Multi-scale Feature Fusionmentioning

confidence: 99%

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Wu,

Chen,

et al. 2024

Metals

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“…BiFPN is a novel network structure for multi-scale feature fusion [31][32][33] that addresses the issue of traditional one-way FPN not fully utilizing different scale feature information. BiFPN adds a bottom-up feature path to the FPN and achieves multi-scale feature fusion through bidirectional connections and feature fusion on feature nodes in the feature pyramid network, resulting in improved accuracy and efficiency.…”

Section: Bifpnmentioning

confidence: 99%

Enhanced YOLOv5: An Efficient Road Object Detection Method

Chen,

2023

Sensors

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Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition.

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An enhanced YOLOv8‐based bolt detection algorithm for transmission line

Hua,

Zhang,

Huang

et al. 2024

IET Generation Trans & Dist

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The current bolt detection for overhead work robots used for transmission lines faces the problems of lightweight algorithms and high accuracy of target detection. To address these challenges, this paper proposes a lightweight bolt detection algorithm based on improved YOLOv8 (you only look once v8) model. Firstly, the C2f module in the feature extraction network is integrated with the self‐calibrated convolution module, and the model is streamlined by reducing spatial and channel redundancies of the network through the SRU and CUR mechanisms in the module. Secondly, the P2 small object detection layer is introduced into the neck structure and the BiFPN network structure is incorporated to enhance the bidirectional connection paths, thereby promoting the upward and downward propagation of features. It improves the accuracy of the network for bolt‐small target detection. The experimental results show that, compared to the original YOLOv8 model, the proposed algorithm demonstrates superior performance on a self‐collected dataset. The mAP accuracy is improved in this paper by 9.9%, while the number of model parameters and the model size is reduced by 0.973 × 106 and 1.7 MB, respectively. The improved algorithm improves the accuracy of the bolt detection while reducing the computation complexity to achieve more lightweight model.

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ENGD-BiFPN: a remote sensing object detection model based on grouped deformable convolution for power transmission towers

Cited by 6 publications

References 32 publications

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

SDD-YOLO: A Lightweight, High-Generalization Methodology for Real-Time Detection of Strip Surface Defects

Enhanced YOLOv5: An Efficient Road Object Detection Method

An enhanced YOLOv8‐based bolt detection algorithm for transmission line

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