Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

Zhang, Heng; Shao, Faming; Chu, Weijun; Dai, Juying; Li, Xingde; Zhang, Xiangpo; Gong, Congcong

doi:10.1007/s11760-024-03370-3

SIViP

2024

DOI: 10.1007/s11760-024-03370-3

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Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

Heng Zhang,

Faming Shao,

Weijun Chu

et al.

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2024

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Cited by 2 publications

References 27 publications

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Comparison of CNN-Based Architectures for Detection of Different Object Classes

Bilous,

Malko,

Frohme

et al. 2024

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(1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. (4) Conclusion: Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1-0.88, and FPS-48, so the focus was on these models.

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Comparison of CNN-Based Architectures for Detection of Different Object Classes

Bilous,

Malko,

Frohme

et al. 2024

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YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism

Zhao,

Fu,

Jia

et al. 2024

Sensors

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In intelligent transportation systems, accurate vehicle target recognition within road scenarios is crucial for achieving intelligent traffic management. Addressing the challenges posed by complex environments and severe vehicle occlusion in such scenarios, this paper proposes a novel vehicle-detection method, YOLO-BOS. First, to bolster the feature-extraction capabilities of the backbone network, we propose a novel Bi-level Routing Spatial Attention (BRSA) mechanism, which selectively filters features based on task requirements and adjusts the importance of spatial locations to more accurately enhance relevant features. Second, we incorporate Omni-directional Dynamic Convolution (ODConv) into the head network, which is capable of simultaneously learning complementary attention across the four dimensions of the kernel space, therefore facilitating the capture of multifaceted features from the input data. Lastly, we introduce Shape-IOU, a new loss function that significantly enhances the accuracy and robustness of detection results for vehicles of varying sizes. Experimental evaluations conducted on the UA-DETRAC dataset demonstrate that our model achieves improvements of 4.7 and 4.4 percentage points in mAP@0.5 and mAP@0.5:0.95, respectively, compared to the baseline model. Furthermore, comparative experiments on the SODA10M dataset corroborate the superiority of our method in terms of precision and accuracy.

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Faster R-CNN based on frame difference and spatiotemporal context for vehicle detection

Cited by 2 publications

References 27 publications

Comparison of CNN-Based Architectures for Detection of Different Object Classes

Comparison of CNN-Based Architectures for Detection of Different Object Classes

YOLO-BOS: An Emerging Approach for Vehicle Detection with a Novel BRSA Mechanism

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