Driver Assistance in Fog Environment Based on Convolutional Neural Networks (CNN)

Allach, Samir; Mohamed, Ben Ahmed; Abdelhakim, Boudhir Anouar

doi:10.1007/978-3-030-11196-0_83

Cited by 2 publications

(1 citation statement)

References 16 publications

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“…Guo et al (2022) first proposed a data set for vehicle detection on foggy highway, and then proposed a foggy vehicle detection model based on improved generative adversarial network and YOLOv4, which effectively improves vehicle detection performance and has strong universality for low-visibility applications based on computer vision. Samir et al (2018) proposed a methodology for target detection during foggy days. The model employed convolutional neural networks for image removal and Fast R-CNN for target detection.…”

Section: Related Work Target Detection In Inclement Weather Conditionsmentioning

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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Section: Related Work Target Detection In Inclement Weather Conditionsmentioning

confidence: 99%