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Study on the Dynamic Detection Algorithm for Risk Targets in the Blind Spot of Right-Turning Dump Trucks
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Penglin He1, Zhifang Chen2, Chang Wang3
Automotive Engineer | 2024, (8) : 36 - 41
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Automotive Engineer | 2024, (8): 36-41
• Special Issue on Intelligent Vehicle Environmental Perception and Target Detection Technology •
Study on the Dynamic Detection Algorithm for Risk Targets in the Blind Spot of Right-Turning Dump Trucks
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Penglin He1, Zhifang Chen2, Chang Wang3
Affiliations
  • 1 Shenzhen Smart Chelian Technology Co., Ltd., Shenzhen 518100
  • 2 Zhejiang Haikang Technology Co., Ltd., Hangzhou 310000
  • 3 Chang’an University, Xi’an 710064
Published: 2024-08-15 doi: 10.20104/j.cnki.1674-6546.20240167
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To address the issue of extensive blind spots during right turns due to the oversized nature of dump trucks, this paper proposes a dynamic detection algorithm for risk targets in the right-turn blind spots of dump trucks. The algorithm improves the YOLOv8 model by enhancing the C2f module and lossing calculation module to refine the model’s detection accuracy. Additionally, four position threshold lines are preset in the blind spots, the risk warning module of the blind spots of the dump truck is added, and the auxiliary driving system of the blind spots of the dump truck is established. The results indicate that the proposed dynamic detection algorithm can recognize various types of targets, including cars, trucks, buses, pedestrians and electric bicycles, with a mean Average Precision (mAP50) of 0.87 at a 50% intersection over union threshold for all categories of targets. The right-turn blind spots assisted driving system of the dump truck can make different degrees of early warning according to the position of the risk target box in the image.

Dump truck blind area  /  Object detection  /  YOLOv8  /  Warning system
Penglin He, Zhifang Chen, Chang Wang. Study on the Dynamic Detection Algorithm for Risk Targets in the Blind Spot of Right-Turning Dump Trucks[J]. Automotive Engineer, 2024 , (8) : 36 -41 . DOI: 10.20104/j.cnki.1674-6546.20240167
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Year 2024 volume Issue 8
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doi: 10.20104/j.cnki.1674-6546.20240167
  • Online Date：2025-11-25
  • Published：2024-08-15
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  • Revised：2024-06-10
Affiliations
    1 Shenzhen Smart Chelian Technology Co., Ltd., Shenzhen 518100
    2 Zhejiang Haikang Technology Co., Ltd., Hangzhou 310000
    3 Chang’an University, Xi’an 710064
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
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Genus		 种数
Number of
species		 占总种数比例
Percentage of total
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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