Clustering based detection of small target pedestrians for smart cities

Yuan, Guohui; Ye, Tao; Fu, Hangming; Wang, Lina; Wang, Zhuoran

doi:10.1016/j.seta.2022.102300

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…is a branch of object detection research, several object detection algorithms are optimized specifically for pedestrian characteristics. For example, Yuan et al [10] propose a detection algorithm for pedestrians with a small resolution. This algorithm generates object cluster regions through cluster detection, and then provides object scale estimates for these regions, and finally outputs the detection results.…”

Section: Pedestrian Detection Model Pedestrian Detectionmentioning

confidence: 99%

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Research on Pedestrian Detection Algorithm in Industrial Scene Based on Improved YOLOv7‐Tiny

Wang,

Bai,

Wang

et al. 2024

IEEJ Transactions Elec Engng

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YOLOv7 is one of the most effective algorithms for one‐stage detectors. However, when it is applied to pedestrian detection tasks in the industrial scene, it is still challenging for complex environments and multi‐scale changes of pedestrians. This paper proposes a new pedestrian detector for the industrial scene based on improved YOLOv7‐tiny and named as GP‐YOLO. First, the neck of YOLOv7‐tiny is replaced by RepGFPN structure, make full use of multi‐scale features to enhance the detection accuracy of objects with large‐scale changes. Second, a new gnconv branch is added to the feature fusion module, and the high‐order spatial interaction capability is introduced to further enhance the target detection accuracy. Finally, a lightweight method based on PModule is proposed, on this basis, a PConv bottleneck is designed to reduce the FLOPs and enhance the feature extraction. Experiments on a self‐made Industrial Pedestrian Data set show that before lightweight, the proposed algorithm achieves a 3.2% improvement in mAP@0.5:0.95 and a 3.7% improvement in Recall compared to the baseline YOLOv7‐tiny. After lightweight GP‐YOLO, compared to non‐lightweight, parameters and FLOPs are decreased by 26% and 23%, respectively, the mAP@0.5:0.95 is decreased by only 1.1% and the Recall is decreased by only 1.3%, which remains at a high level. Compared with baseline YOLOv7‐tiny, the lightweight GP‐YOLO has similar parameters and FLOPs, but the mAP@0.5:0.95 is increased by 2.1%, and the Recall is increased by 2.4%. © 2024 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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Section: Pedestrian Detection Model Pedestrian Detectionmentioning

confidence: 99%

“…For example, Yuan et al . [10] propose a detection algorithm for pedestrians with a small resolution. This algorithm generates object cluster regions through cluster detection, and then provides object scale estimates for these regions, and finally outputs the detection results.…”

Section: Related Workmentioning

confidence: 99%

Research on Pedestrian Detection Algorithm in Industrial Scene Based on Improved YOLOv7‐Tiny

Wang,

Bai,

Wang

et al. 2024

IEEJ Transactions Elec Engng

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“…To solve these problems, the researchers optimize the small object detection method based on various optimization strategies, such as data enhancement [14] , [15] , [16] , [17] , [18] , multi-scale learning [19] , [20] , [21] , [22] , context learning [23] , [24] , [25] , [26] , [27] , and generative confrontation learning [28] , [29] , [30] , [31] , [32] , [33] , [34] , which are analyzed as follows:…”

Section: Introductionmentioning

confidence: 99%

Small object intelligent detection method based on adaptive recursive feature pyramid

Zhang

Liu

et al. 2023

Heliyon

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“…An important aspect in ensuring the safety of pedestrians in transport networks is the detection and recognition of pedestrians [9] and the automatic analysis of human behaviour [1]. Technological progress has resulted in the increasingly frequent use of cameras in road traffic as sensors that enable video analysis of roads and intersections [10].…”

Section: Introductionmentioning

confidence: 99%

Pedestrian Safety in Road Traffic in the Era of the SARS-CoV-2 Pandemic in the Example of Szczecin

2023

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Safety of road users is one of the priority tasks of state institutions and local government authorities. Compared to other EU member states, Poland is characterized by a low level of road safety. According to the World Health Organization (WHO), more than half of road fatalities affect vulnerable road users (VRU). Most interactions in the vehicle–VRU relationship occur in built-up areas. Limiting the public’s mobility related to the spread of the COVID-19 virus led to changes in road traffic, which was reflected in the level of safety of individual road users. As part of a clear emphasis on the issue of VRU protection, the aim of this article is to examine the level of pedestrian safety in urban space at the time of restrictions on the public’s mobility in Poland during the SARS-CoV-2 pandemic and after lifting the limitations, based on the example of Szczecin. The authors put forward two research hypotheses: (1) the level of road safety is influenced by the intensity and saturation of the transport network; (2) the intensity of road traffic determines the occurrence of a greater number of road accident consequences. This study analyses traffic accident data over three periods—2017–2018, 2019–2020, and 2021—to identify pedestrian safety trends. The research shows that the intensity of road traffic has a significant impact on the occurrence of road accidents. The reduced road traffic due to the movement restrictions has kept the severity of road accidents moderate. In the post-pandemic period and the gradual lifting of the mobility restrictions, the number of road accidents increased while their consequences decreased.

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Clustering based detection of small target pedestrians for smart cities

Cited by 5 publications

References 10 publications

Research on Pedestrian Detection Algorithm in Industrial Scene Based on Improved YOLOv7‐Tiny

Research on Pedestrian Detection Algorithm in Industrial Scene Based on Improved YOLOv7‐Tiny

Small object intelligent detection method based on adaptive recursive feature pyramid

Pedestrian Safety in Road Traffic in the Era of the SARS-CoV-2 Pandemic in the Example of Szczecin

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