Research on Vehicle Object Detection Algorithm Based on Improved YOLOv3 Algorithm

Liu, Jin; Zhang, Dongquan

doi:10.1088/1742-6596/1575/1/012150

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…Reference [42] improved the multiscale predictive network model based on actual traffic conditions and efficiency requirements, which resulted in improved detection accuracy and speed. They achieved this by adding scale prediction layers to enhance the detection accuracy of large vehicles and by using an improved k-means++ algorithm for anchor box dimension clustering to boost detection speed and effectiveness.…”

Section: Vehicle Detectionmentioning

confidence: 99%

A Review of Object Detection in Traffic Scenes Based on Deep Learning

Zhao,

Tang,

Supeni

et al. 2024

Applied Mathematics and Nonlinear Sciences

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At the current stage, the rapid Development of autonomous driving has made object detection in traffic scenarios a vital research task. Object detection is the most critical and challenging task in computer vision. Deep learning, with its powerful feature extraction capabilities, has found widespread applications in safety, military, and medical fields, and in recent years has expanded into the field of transportation, achieving significant breakthroughs. This survey is based on the theory of deep learning. It systematically summarizes the Development and current research status of object detection algorithms, and compare the characteristics, advantages and disadvantages of the two types of algorithms. With a focus on traffic signs, vehicle detection, and pedestrian detection, it summarizes the applications and research status of object detection in traffic scenarios, highlighting the strengths, limitations, and applicable scenarios of various methods. It introduces techniques for optimizing object detection algorithms, summarizes commonly used object detection datasets and traffic scene datasets, along with evaluation criteria, and performs comparative analysis of the performance of deep learning algorithms. Finally, it concludes the development trends of object detection algorithms in traffic scenarios, providing research directions for intelligent transportation and autonomous driving.

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Section: Vehicle Detectionmentioning

confidence: 99%

A Review of Object Detection in Traffic Scenes Based on Deep Learning

Zhao,

Tang,

Supeni

et al. 2024

Applied Mathematics and Nonlinear Sciences

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“…The one-stage detection algorithm [29,30] requires only one process to achieve detection. Feature extraction, target classification, and location regression are carried out in the whole convolutional network, and the target position and class can be obtained through one reverse calculation.…”

Section: Red Fruit Target Detection Schemementioning

confidence: 99%

Research on the Real-Time Detection of Red Fruit Based on the You Only Look Once Algorithm

Mei,

Ding,

Wang

2023

Processes

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The real-time and accurate recognition of fruits and vegetables is crucial for the intelligent control of fruit and vegetable robots. In this research, apple picking is selected. This study proposed a lightweight, coupon-product, neural-net terminal YOLO algorithm for apple image recognition. Compared with the YOLO series algorithm, the tiny algorithm shows a strong relationship with the calculation speed. In traditional red fruit detection, the recognition time is generally several seconds, which is unacceptable in the real-time system. In this research, a total of 2000 apple images from different environments are used as a dataset for training and testing. The YOLOv4-tiny model is detailed, instructed, and used for the identification. The indicators, such as F1Score (0.92) and mAP (95.5% average), are analyzed by calculating the loss rate, accuracy rate (96.21%), and recall rate (95.47%). Finally, the algorithm shows good accuracy and high speed (no more than 5 ms) in online real-time detection.

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“…In order to focus on solving the errors in the recall rate and positioning accuracy of YOLOv1, an improved YOLOv2 detection algorithm was proposed in 2016, which uses darknet-19 as the feature extraction network [31]. A variety of strategies, such as using anchor box, multi-scale training, and batch standardization processing, were proposed to improve the mean average precision (mAP) detected by the algorithm.…”

Section: Yolo Principlementioning

confidence: 99%

A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

Shi

Tang²

2022

Journal of Electrical and Computer Engineering

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Deep learning theory is widely used in face recognition. Combined with the needs of classroom attendance and students’ learning status monitoring, this article analyzes the YOLO (You Only Look Once) face recognition algorithms based on regression method. Aiming at the problem of small target missing detection in the YOLOv3 network structure, an improved YOLOv3 algorithm based on Bayesian optimization is proposed. The algorithm uses deep separable convolution instead of conventional convolution to improve the Darknet-53 basic network, and it reduces the amount of calculation and parameters of the network. A multiscale feature pyramid is built, and an attention guidance module is designed to strengthen multiscale fusion, detecting different sizes of targets. The loss function is improved to solve the imbalance of positive and negative sample distribution and the imbalance between simple samples and difficult samples. The Bayesian function is adopted to optimize the classifier and improve the classification efficiency and accuracy, ensuring the accuracy of small target detection. Five groups of comparative experiments are carried out on public COCO and VOC2012 datasets and self-built datasets. The experimental results show that the proposed improved YOLOv3 model can effectively improve the detection accuracy of multiple faces and small targets. Compared with the traditional YOLOv3 model, the mean mAP of the target is improved by more than 1.2%.

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Research on Vehicle Object Detection Algorithm Based on Improved YOLOv3 Algorithm

Cited by 15 publications

References 7 publications

A Review of Object Detection in Traffic Scenes Based on Deep Learning

A Review of Object Detection in Traffic Scenes Based on Deep Learning

Research on the Real-Time Detection of Red Fruit Based on the You Only Look Once Algorithm

A New Multiface Target Detection Algorithm for Students in Class Based on Bayesian Optimized YOLOv3 Model

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