YOLOv3-DPFIN: A Dual-Path Feature Fusion Neural Network for Robust Real-Time Sonar Target Detection

Kong, Wanzeng; Hong, Jichen; Jia, Mingyang; Yao, Jinliang; Wei, Cong; Hu, Hua; Zhang, Haigang

doi:10.1109/jsen.2019.2960796

Cited by 71 publications

(29 citation statements)

References 22 publications

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“…In this regard, members of the other group of single-stage object detection models, such as You Only Look Once (YOLO) [11] and Single Shot MultiBox Detector (SSD), perform better by directly adopting a regression method for object detection, resulting in higher operational speed. However, SSD does not consider the relationship between different scales, so it has limitations in detecting small objects, whereas YOLO is easier to learn general features, and its operational speed is higher [12]. Both SSD and YOLO, however, cannot perfectly handle the graphic area, resulting in high detection error and missing rates.…”

Section: Introductionmentioning

confidence: 99%

Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4

et al. 2022

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Rapid and precise detection and classification of vehicles are vital for the intelligent transportation systems (ITSs). However, due to small gaps between vehicles on the road and interference features of photos, or video frames, containing vehicle images, it is difficult to detect and identify vehicle types quickly and precisely. For solving this problem, a new vehicle detection and classification model, named YOLOv4_AF, is proposed in this paper, based on an optimization of the YOLOv4 model. In the proposed model, an attention mechanism is utilized to suppress the interference features of photos through both the channel dimension and spatial dimension. In addition, a modification on the Feature Pyramid Network (FPN) part of the Path Aggregation Network (PAN), utilized by YOLOv4, is applied in order to enhance further the effective features through down-sampling. This way, the objects can be steadily positioned in the 3D space and the object detection and classification performance of the model can be improved. The results, obtained through experiments conducted on two public data sets, demonstrate that the proposed YOLOv4_AF model outperforms, in this regard, both the original YOLOv4 model and two other state-of-the-art models, Faster R-CNN and EfficientDet, in terms of the mean average precision (mAP) and F1 score, by achieving respective values of 83.45% and 0.816 on the BIT-Vehicle data set, and 77.08% and 0.808 on the UA-DETRAC data set.

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Section: Introductionmentioning

confidence: 99%

Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4

et al. 2022

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“…In the bounding box regression branch of the FCOS detection head, the original IoU Loss has been replaced with DIoU to better detect objects with smaller sizes, and mAP reached 39.3%. The In Table 4, comparing our method with the object detection algorithms which commonly used in the industry: Faster R-CNN, YOLO v3 [17] and YOLO v3-Tiny [18]. Compared with Faster R-CNN, our method has a lower mAP, but it can maintain the detection speed while having better accuracy.…”

Section: Tests and Resultsmentioning

confidence: 99%

A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN

et al. 2021

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Normally functioning and complete printed circuit board (PCB) can ensure the safety and reliability of electronic equipment. PCB defect detection is extremely important in the field of industrial inspection. For traditional methods of PCB inspection, such as contact detection, are likely to damage the PCB surface and have high rate of erroneous detection. In recent years, methods of detection through image processing and machine learning have gradually been put into use. However, PCB inspection is still an extremely challenging task due to the small defects and the complex background. To solve this problem, a lightweight one-stage defect detection network based on dual attention mechanism and Path Aggregation Feature Pyramid Network (PAFPN) has been proposed. At present, some methods of defect detection in industrial applications are often based on object detection algorithms in the field of deep learning. Through comparative experiments, compared with the Faster R-CNN and YOLO v3 which are usually used in the current industrial detection, the inference time of our method are reduced by 17.46 milliseconds (ms) and 4.75 ms, and the amount of model parameters is greatly reduced. It is only 4.42 M, which is more suitable for industrial fields and embedded development systems. Compared with the common one-stage object detection algorithm Fully Convolutional One-Stage Object Detection (FCOS), mean Average Precision (mAP) is increased by 9.1%, and the amount of model parameters has been reduced by 86.12%.

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“…Xiong et al proposed an anchor box and YOLOv3-Darknet model based on adaptive data clustering to identify, classify, and detect dry and wet garbage [ 27 ]. Kong et al proposed a model in Darknet-53 that conducts efficient feature extraction via the Dual-Path Network (DPN) module and the fusion transition module during the real-time sonar target detection [ 28 ]. Zhang and Zhu replaced the original Darknet-53 with the Darknet-23 to improve the detection speed when detecting moving vehicles in aerial infrared image sequences [ 29 ].…”

Section: Related Workmentioning

confidence: 99%

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

Chen

et al. 2021

Computational Intelligence and Neuroscience

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Vision-based recognizing and positioning of electronic components on the PCB (printed circuit board) can improve the quality inspection efficiency of electronic products in the manufacturing process. With the improvement of the design and the production process, the electronic components on the PCB show the characteristics of small sizes and similar appearances, which brings challenges to visual object detection. This paper designs a real-time electronic component detection network through effective receptive field size and anchor size matching in YOLOv3. We make contributions in the following three aspects: (1) realizing the calculation and visualization of the effective receptive field size of the different depth layers of the CNN (convolutional neural network) based on gradient backpropagation; (2) proposing a modular YOLOv3 composition strategy that can be added and removed; and (3) designing a lightweight and efficient detection network by effective receptive field size and anchor size matching algorithm. Compared with the Faster-RCNN (regions with convolutional neural network) features, SSD (single-shot multibox detectors), and original YOLOv3, our method not only has the highest detection mAP (mean average precision) on the PCB electronic component dataset, which is 95.03%, the smallest parameter size of the memory, about 1/3 of the original YOLOv3 parameter amount, but also the second-best performance on FLOPs (floating point operations).

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YOLOv3-DPFIN: A Dual-Path Feature Fusion Neural Network for Robust Real-Time Sonar Target Detection

Cited by 71 publications

References 22 publications

Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4

Improved Vision-Based Vehicle Detection and Classification by Optimized YOLOv4

A Lightweight One-Stage Defect Detection Network for Small Object Based on Dual Attention Mechanism and PAFPN

A PCB Electronic Components Detection Network Design Based on Effective Receptive Field Size and Anchor Size Matching

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