Vehicle detection method with low-carbon technology in haze weather based on deep neural network

Ning, Tao; Xiangkun, Jia; Duan, Xiaodong; Song, Jing; Ranran, Liang

doi:10.1093/ijlct/ctac084

Cited by 9 publications

(3 citation statements)

References 14 publications

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“…Deep learning approaches are replacing traditional perception tasks, such as object detection, tracking, etc., with newer, more potent ones as a result of the development of machine learning and AI (artificial intelligence) technologies. Deep learning frameworks were utilized by [7] to detect vehicles in foggy conditions. Ref.…”

Section: Object Detectionmentioning

confidence: 99%

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Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8

Kumar,

Muhammad

2023

Sensors

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For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is challenging, wherein it suffers from dynamic objects and continuous environmental changes. The issue grows worse due to interrupting the quality of perception via adverse weather such as snow, rain, fog, night light, sand storms, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous-driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights was collected from training on the individual datasets, their merged versions, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the datasets mentioned earlier and their subsets. The evaluation revealed that using custom datasets for training significantly improved the detection performance compared to the YOLOv8 base weights. Furthermore, using more images through the feature-related data merging technique steadily increased the object detection performance.

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Section: Object Detectionmentioning

confidence: 99%

“…Ref. [7] used an attention module to better concentrate on prospective information during feature extraction. Ref.…”

Section: Object Detectionmentioning

confidence: 99%

Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8

Kumar,

Muhammad

2023

Sensors

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“…Guo et al ( 2022 ) proposed a domain-adaptive road vehicle target detection method based on an improved CycleGAN network and YOLOv4 to improve the vehicle detection performance and the generalization ability of the model under low-visibility weather conditions. Tao et al ( 2022 ) improved YOLOv3 based on ResNet, and the improved network reduced the difficulty of vehicle detection in hazy weather and improved the detection accuracy. Humayun et al ( 2022 ) proposed an improved CSPDarknet53 network to enhance the detection precision of targets in the haze, dust storms, snow, and rain weather conditions during day and night.…”

Section: Related Workmentioning

confidence: 99%

Real-time vehicle target detection in inclement weather conditions based on YOLOv4

Wang

Zhao

et al. 2023

Front. Neurorobot.

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As a crucial component of the autonomous driving task, the vehicle target detection algorithm directly impacts driving safety, particularly in inclement weather situations, where the detection precision and speed are significantly decreased. This paper investigated the You Only Look Once (YOLO) algorithm and proposed an enhanced YOLOv4 for real-time target detection in inclement weather conditions. The algorithm uses the Anchor-free approach to tackle the problem of YOLO preset anchor frame and poor fit. It better adapts to the detected target size, making it suitable for multi-scale target identification. The improved FPN network transmits feature maps to unanchored frames to expand the model's sensory field and maximize the utilization of model feature data. Decoupled head detecting head to increase the precision of target category and location prediction. The experimental dataset BDD-IW was created by extracting specific labeled photos from the BDD100K dataset and fogging some of them to test the proposed method's practical implications in terms of detection precision and speed in Inclement weather conditions. The proposed method is compared to advanced target detection algorithms in this dataset. Experimental results indicated that the proposed method achieved a mean average precision of 60.3%, which is 5.8 percentage points higher than the original YOLOv4; the inference speed of the algorithm is enhanced by 4.5 fps compared to the original, reaching a real-time detection speed of 69.44 fps. The robustness test results indicated that the proposed model has considerably improved the capacity to recognize targets in inclement weather conditions and has achieved high precision in real-time detection.

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A closer look at single object tracking under variable haze

Singh,

Lamba,

Khosla

2024

Multimed Tools Appl

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Vehicle detection method with low-carbon technology in haze weather based on deep neural network

Cited by 9 publications

References 14 publications

Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8

Object Detection in Adverse Weather for Autonomous Driving through Data Merging and YOLOv8

Real-time vehicle target detection in inclement weather conditions based on YOLOv4

A closer look at single object tracking under variable haze

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