Vehicle Type Recognition Algorithm Based on Improved Network in Network

Zhu, Erxi; Xu, Min; Pi, De Chang

doi:10.1155/2021/6061939

Cited by 5 publications

(2 citation statements)

References 38 publications

(40 reference statements)

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“…In this short communication, we propose a feasible solution for heavy goods vehicle detection. Computer Vision algorithms have been implemented for various tasks in traffic monitoring for many years, e.g., traffic sign recognition [1][2][3][4][5][6][7]; intelligent traffic light system [8]; vehicle speed monitoring [9]; traffic violation monitoring [10]; vehicle tracking [11][12][13]; vehicle classification [14][15][16][17][18][19][20][21][22][23][24][25][26]; vehicle counting system on streets and highways [27][28][29][30][31]; parking spot detection from the point of view of the car for parking assistants [32,33]; and parking spot monitoring [34][35][36][37][38][39][40][41][42][43][44][45][46][47]…”

Section: Introductionmentioning

confidence: 99%

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

et al. 2021

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The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

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Section: Introductionmentioning

confidence: 99%

Short Communication: Detecting Heavy Goods Vehicles in Rest Areas in Winter Conditions Using YOLOv5

et al. 2021

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“…Network in Network (NIN) Model for Vehicle Type RecognitionZhu et. al[24] proposed a novel deep network model for vehicle type recognition based on NIN[25] deep model (see in Fig.1). They address the low identification problem of vehicle type recognition to feature representation.…”

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confidence: 99%

Review of Vehicle Type Recognition Methods

Lin¹

2023

HSET

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With the booming social and economic development, the number of domestic and foreign motor vehicles has increased dramatically, resulting in increasing traffic flow, traffic congestion, and traffic safety problems. To better relieve traffic pressure, intelligent transportation has become the mainstream development direction and an important means of modern transportation, of which vehicle type recognition is the core component of intelligent transportation. Therefore, the research on vehicle identification is of great significance in both scientific research and real-life applications, and it is also a challenging task. Among the various detection and recognition methods, deep learning has been proved to be a more effective one, so this paper discusses the current research on deep learning and vehicle model recognition.

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