Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

Bourja, Omar; Derrouz, Hatim; Abdelali, Hamd Ait; Maach, Abdelilah; Thami, Rachid Oulad Haj; Bourzeix, François

doi:10.14569/ijacsa.2021.01208101

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…In FPN [4], since region proposal network is applied on the feature pyramid, anchor boxes at each spatial location of a feature level are defined using one scale and three aspect ratios. Anchor-based scheme has been employed in many deep object detection frameworks [5], [6], [7], [31]. However, anchor box scales and aspect ratios must be meticulously designed for the specific domain to ensure the detection network attains optimal detection performance.…”

Section: A Anchor-based Object Detection Methodsmentioning

confidence: 99%

Anchor-free Proposal Generation Network for Efficient Object Detection

Nguyen¹

2023

IJACSA

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Deep learning object detection methods are usually based on anchor-free or anchor-based scheme for extracting object proposals and one-stage or two-stage structure for producing final predictions. As each scheme or structure has its own strength and weakness, combining their strength in a unified framework is an interesting research topic. However, this topic has not attracted much attention in recent years. This paper presents a two-stage object detection method that utilizes an anchor-free scheme for generating object proposals in the initial stage. For proposal generation, this paper employs an efficient anchor-free network for predicting object corners and assigns object proposals based on detected corners. For object prediction, an efficient detection network is designed to enhance both detection accuracy and speed. The detection network includes a lightweight binary classification subnetwork for removing most false positive object candidates and a light-head detection subnetwork for generating final predictions. Experimental results on the MS-COCO dataset demonstrate that the proposed method outperforms both anchor-free and twostage object detection baselines in terms of detection performance.

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Section: A Anchor-based Object Detection Methodsmentioning

confidence: 99%

Anchor-free Proposal Generation Network for Efficient Object Detection

Nguyen¹

2023

IJACSA

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“…(7,8) CNNs can learn highly complex features directly from pixel data without hand-engineering. (9,10) Some pioneering networks like OverFeat (1) and R-CNN (2) demonstrated CNNs' potential for vehicle detection. R-CNN introduced a region-based two-stage CNN approach using selective search and SVMs.…”

Section: Literaturementioning

confidence: 99%

“…SSD achieved accuracy competitive with two-stage methods while enabling real-time processing over 30 FPS. DSSD (8) and RetinaNet (9) built upon the SSD framework with improved accuracy through context modules and focal loss respectively.…”

Section: 1 %mentioning

confidence: 99%

Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach

Swathi,

Tejaswi,

Khan

et al. 2024

Data and Metadata

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Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each technique has tradeoffs between precision and processing speed. Two-stage detectors like Faster R-CNN are highly accurate but slow at 7 FPS. Single-shot detectors like SSD are faster at 22 FPS but less precise. YOLO is extremely fast at 45 FPS but has lower accuracy. This paper reviews prominent deep learning vehicle detectors. It proposes a new integrated method combining YOLOv3 detection, optical flow tracking and trajectory analysis to enhance both accuracy and speed. Results on highway and urban datasets show improved precision, recall and F1 scores compared to YOLOv3 alone. Optical flow helps filter noise and recover missed detections. Trajectory analysis enables consistent object IDs across frames. Compared to other CNN models, the proposed technique achieves a better balance of real-time performance and accuracy. Occlusion handling and small object detection remain open challenges. In summary, deep learning has enabled major progress but enhancements in model architecture, training data and occlusion handling are needed to realize the full potential for traffic management applications. The integrated method proposed offers improved performance over baseline detectors. We have achieved 99 % accuracy in our project

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“…YOLOv3 has also been used for other detections, for example, to detect pedestrians [11], vehicles [12], and road objects [13]. Another investigation compared YOLOv5, YOLOv6, and YOLOv7 for detecting small objects [14], while YOLOv7 was used for real-time weed detection [15].…”

Section: Introductionmentioning

confidence: 99%

Automated Detection of Driver and Passenger Without Seat Belt using YOLOv8

Sugiharto,

Kusumaningrum

2023

IJACSA

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The issue of traffic accident fatalities is a serious concern on a global scale, and one of the contributing factors is the failure of drivers to adhere to seat belt usage. A notable challenge arises from the limited availability of law enforcement personnel monitoring this particular issue. In this context, there is a compelling need to implement an automated detection system. The development of this system using YOLOv5 has been done. However, there are weaknesses related to the length of training and detection time. Therefore, this paper proposed a new system using the YOLOv8 method to detect drivers and passengers who violate seat belt regulations. The proposed system is divided into three subsystems: windshield detection, passenger classification, and seat belt classification. YOLOv8 is the latest version of the YOLO (You Only Look Once) method and has been proven to provide better performance than previous versions. Furthermore, this paper also compared five YOLOv8 models, namely YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. The proposed model is trained and tested using image data collected from several roads in Indonesia. The experiment results show that the YOLOv8s model produced the best mean Average Precision (mAP) of 0.960 for windshield detection. YOLOv8s-cls and YOLOv8l-cls models achieved the same accuracy of 0.8923 for passenger classification. The YOLOv8l-cls model produced the best accuracy of 0.8846 for seat belt classification. In addition, the proposed method can increase mAP and training time for windshield detection compared to YOLOv5.

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Real Time Vehicle Detection, Tracking, and Inter-vehicle Distance Estimation based on Stereovision and Deep Learning using YOLOv3

Cited by 7 publications

References 12 publications

Anchor-free Proposal Generation Network for Efficient Object Detection

Anchor-free Proposal Generation Network for Efficient Object Detection

Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach

Automated Detection of Driver and Passenger Without Seat Belt using YOLOv8

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