PVformer: Pedestrian and Vehicle Detection Algorithm Based on Swin Transformer in Rainy Scenes

Sun, Zaiming; Liu, Changan; Qu, Hongquan; Xie, Guangda

doi:10.3390/s22155667

Cited by 9 publications

(3 citation statements)

References 57 publications

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“…To evaluate the effectiveness of the RSTDet-Lite algorithm, we conduct a performance comparison on the RainDet3000 dataset and select some mainstream algorithms for comparison, including YOLOv4, YOLOv5x, yolov7, and the algorithms proposed by Sun Hao [14], Zhang Baopeng [16], and PVformer [21] as shown in Table 5. The results demonstrate the outstanding performance of RSTDet-Lite in multiple aspects.…”

Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

“…Zhao [20] proposed a lightweight detection model based on YOLOv5, which combines the MD-SILBP operator and the five-frame differential method to enhance the contour feature extraction capability and uses Distance-IoU non-maximum suppression to reduce the missed detection rate in detection. Sun, Zaiming [21] proposed the PVformer algorithm for vehicle and pedestrian detection in rainy scenarios, based on the Swin transformer. They introduced a local enhancement perception block and a deraining module to improve the accuracy of detection in rainy scenes.…”

Section: Introductionmentioning

confidence: 99%

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A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x

Chen,

Wang,

Zhang

2023

Applied Sciences

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Pedestrian detection is a crucial task in computer vision, with various applications in surveillance, autonomous driving, and robotics. However, detecting pedestrians in complex scenarios, such as rainy days, remains a challenging problem due to the degradation of image quality and the presence of occlusions. To address this issue, we propose RSTDet-Lite (a robust lightweight network) for pedestrian detection on rainy days, based on an improved version of YOLOv5-x. Specifically, in order to reduce the redundant parameters of the YOLOv5-x backbone network and enhance its feature extraction capability, we propose a novel approach named CBP-GNet, which incorporates a compact bilinear pooling algorithm. This new net serves as a new backbone network, resulting in significant parameter reduction and enhancing the fine-grained feature fusion capability of the network. Additionally, we introduce the Simple-BiFPN structure as a replacement for the original feature pyramid module based on the weighted bidirectional feature pyramid to further improve feature fusion efficiency. To enhance network performance, we integrate the CBAM attention mechanism and introduce the idea of structural reparameterization. To evaluate the performance of our method, we create a new dataset named RainDet3000, which consists of 3000 images captured in various rainy scenarios. The experimental results demonstrate that, compared with YOLOv5, our proposed model reduces the network size by 30 M while achieving a 4.56% increase in mAP. This confirms the effectiveness of RSTDet-Lite in achieving excellent performance in rainy-day pedestrian detection scenarios.

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Section: Analysis Of Experimental Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Robust Lightweight Network for Pedestrian Detection Based on YOLOv5-x

Chen,

Wang,

Zhang

2023

Applied Sciences

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“…A pedestrian and vehicle detection algorithm is presented in [ 28 ] that utilizes the Swin Transformer. An end-to-end vehicle detection algorithm, PVformer is proposed for enhanced vehicle and pedestrian detection accuracy.…”

Section: Related Workmentioning

confidence: 99%

Analyzing Performance of YOLOx for Detecting Vehicles in Bad Weather Conditions

Ashraf,

Hur,

Kim

et al. 2024

Sensors

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Recent advancements in computer vision technology, developments in sensors and sensor-collecting approaches, and the use of deep and transfer learning approaches have excelled in the development of autonomous vehicles. On-road vehicle detection has become a task of significant importance, especially due to exponentially increasing research on autonomous vehicles during the past few years. With high-end computing resources, a large number of deep learning models have been trained and tested for on-road vehicle detection recently. Vehicle detection may become a challenging process especially due to varying light and weather conditions like night, snow, sand, rain, foggy conditions, etc. In addition, vehicle detection should be fast enough to work in real time. This study investigates the use of the recent YOLO version, YOLOx, to detect vehicles in bad weather conditions including rain, fog, snow, and sandstorms. The model is tested on the publicly available benchmark dataset DAWN containing images containing four bad weather conditions, different illuminations, background, and number of vehicles in a frame. The efficacy of the model is evaluated in terms of precision, recall, and mAP. The results exhibit the better performance of YOLOx-s over YOLOx-m and YOLOx-l variants. YOLOx-s has 0.8983 and 0.8656 mAP for snow and sandstorms, respectively, while its mAP for rain and fog is 0.9509 and 0.9524, respectively. The performance of models is better for snow and foggy weather than rainy weather sandstorms. Further experiments indicate that enhancing image quality using multiscale retinex improves YOLOx performance.

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