Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

Humayun, Mamoona; Ashfaq, Farzeen; Jhanjhi, N. Z.; Alsadun, Marwah Khalid

doi:10.3390/electronics11172748

Cited by 81 publications

(33 citation statements)

References 122 publications

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“…Zheng et al ( 2022 ) proposed an improved Fast R-CNN convolutional neural network for dim target detection in complex traffic environments, replaced VGG16 in Fast R-CNN with ResNet, adopted the downsampling method and introduced feature pyramid network to generate target candidate boxes to optimize the structure of the convolutional neural network. Humayun et al ( 2022 ) used CSPDarknet53 as the baseline framework, and achieved reliable performance for vehicle detection under scenarios such as rain and snow through space pyramid pool layer and batch normalization layer. 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.…”

Section: Related Workmentioning

confidence: 99%

“…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%

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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 Workmentioning

confidence: 99%

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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“…Humayun et al [3] in their study, evaluated the identification of automobiles in a scene under various weather circumstances, such as fog, sand and dust storms, wintry and wet weather both throughout the day as well as night. Their proposed design includes a layer for spatial pyramid pooling (SPP-NET) plus minimal layers for batch normalization to the CSPDarknet53 basic architecture.…”

Section: Literature Reviewmentioning

confidence: 99%

“…According to statistics, almost 1.2 million individuals pass away on the roads each year. Additionally, estimates indicate that approximately 50 million individuals are hurt in traffic accidents worldwide [3]. However, the occurrence varies by country.…”

Section: Introductionmentioning

confidence: 99%

Sorokh-Poth: A Balanced Bangladeshi Road Vehicle Image Dataset Integrated With a Detection System Based On Deep Convolutional Neural Networks

Sana¹,

Islam²,

Hasan³

et al. 2023

IJRP

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When it comes to the administration of highways, the identification of intelligent vehicles is becoming an increasingly important component. Applications for vehicle detection are essential for both military and civilian usage, including the monitoring of highway traffic, administration, and the control of urban traffic. The vast amount of vehicle image data available online has the potential to encourage the development of increasingly sophisticated object recognition and classification models and algorithms. But finding an organized, balanced, and meaningful dataset continues to be a major challenge. This study proposes "Sorokh-Poth," a new complete balanced image dataset based on Bangladeshi road travel that is compatible with a number of CNN-based architectures. The majority of the photographs in the collection were taken with a smartphone. The dataset includes 9,809 classified and annotated images of ten different types of vehicles, including autorickshaws, bicycles, buses, cars, CNG-powered vehicles, lagoon rickshaws, trucks, and vans. This research work utilizes the residual network ResNet-50 model, a CNN-based architecture. Here, features specific to the type of vehicle were automatically retrieved and grouped. Accuracy, precision, recall, and f1-score were just a few of the metrics used for evaluation during the research. The proposed model exhibited an increasing accuracy despite the vehicles' shifting physical characteristics. The purpose of this work is to implement various CNN-based residual networks ResNet-50 and ResNet-150V2. Our proposed ResNet-50 model achieves an accuracy of 98.00% in the detection of native Bangladeshi vehicles, according to result comparisons and evaluations.

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“…Humayun [25] detecting vehicles in a scene in multiple weather scenarios including haze, dust and sandstorms, snowy and rainy weather both in day and nighttime. Using YOLOv4 and Spatial Pyramid Pooling Network.…”

Section: Introductionmentioning

confidence: 99%

Enhanced aerial vehicle system techniques for detection and tracking in fog, sandstorm, and snow conditions

Talaat

El–Sappagh

2023

J Supercomput

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Unmanned aerial vehicles are rapidly being utilized in surveillance and traffic monitoring because of their great mobility and capacity to cover regions at various elevations and positions. It is a challenging task to detect vehicles due to their various shapes, textures, and colors. One of the most difficult challenges is correctly detecting and counting aerial view vehicles in real time for traffic monitoring objectives using aerial images and videos. In this research, strategies are presented for improving the detection ability of self-driving vehicles in tough conditions, also for traffic monitoring, vehicle surveillance. We make classification, tracking trajectories, and movement calculation where fog, sandstorm (dust), and snow conditions are challenging. Initially, image enhancement methods are implemented to improve unclear images of roads. The improved images are then subjected to an object detection and classification algorithm to detect vehicles. Finally, new methods were evaluated (Corrected Optical flow/Corrected Kalman filter) to get the least error of trajectories. Also features like vehicle count, type, tracking trajectories by (Optical flow, Kalman Filter, Euclidean Distance) and relative movement calculation are extracted from the coordinates of the observed objects. These techniques aim to improve vehicle detection, tracking, and movement over aerial views of roads especially in bad weather. As a result, for aerial view vehicles in bad weather, our proposed method has an error of less than 5 pixels from the actual value and give the best results. This improves detection and tracking performance for aerial view vehicles in bad weather conditions.

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Traffic Management: Multi-Scale Vehicle Detection in Varying Weather Conditions Using YOLOv4 and Spatial Pyramid Pooling Network

Cited by 81 publications

References 122 publications

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

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

Sorokh-Poth: A Balanced Bangladeshi Road Vehicle Image Dataset Integrated With a Detection System Based On Deep Convolutional Neural Networks

Enhanced aerial vehicle system techniques for detection and tracking in fog, sandstorm, and snow conditions

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