Improved traffic sign recognition algorithm based on YOLOv4-tiny

Sharma, Vipul; Dhiman, Pankaj; Rout, Ranjeet Kumar

doi:10.1016/j.jvcir.2023.103774

Cited by 47 publications

(10 citation statements)

References 17 publications

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“…The YOLOv4-Tiny model is a lightweight version of YOLOv4, with slightly reduced accuracy; but the model size is only 1/10 of the original, and the detection speed is significantly improved, which is fully suitable for rapid detection on embedded platforms with low computing resources. Based on this consideration, the YOLOv4-Tiny lightweight network shown in Figure 3 is selected [ 31 ].…”

Section: Methodsmentioning

confidence: 99%

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Pan,

Huang,

Zhu

et al. 2024

Sensors

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Rapid detection of fish freshness is of vital importance to ensuring the safety of aquatic product consumption. Currently, the widely used optical detecting methods of fish freshness are faced with multiple challenges, including low detecting efficiency, high cost, large size and low integration of detecting equipment. This research aims to address these issues by developing a low-cost portable fluorescence imaging device for rapid fish freshness detection. The developed device employs ultraviolet-light-emitting diode (UV-LED) lamp beads (365 nm, 10 W) as excitation light sources, and a low-cost field programmable gate array (FPGA) board (model: ZYNQ XC7Z020) as the master control unit. The fluorescence images captured by a complementary metal oxide semiconductor (CMOS) camera are processed by the YOLOv4-Tiny model embedded in FPGA to obtain the ultimate results of fish freshness. The circuit for the YOLOv4-Tiny model is optimized to make full use of FPGA resources and to increase computing efficiency. The performance of the device is evaluated by using grass carp fillets as the research object. The average accuracy of freshness detection reaches up to 97.10%. Moreover, the detection time of below 1 s per sample and the overall power consumption of 47.1 W (including 42.4 W light source power consumption) indicate that the device has good real-time performance and low power consumption. The research provides a potential tool for fish freshness evaluation in a low-cost and rapid manner.

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Section: Methodsmentioning

confidence: 99%

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Pan,

Huang,

Zhu

et al. 2024

Sensors

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“…Despite these challenges, the YOLO series algorithm strikes a balance between detection speed and accuracy in detecting small targets. Sharma et al 7 proposed yolov4-tiny network, which eliminates lowfrequency feature redundancy by adding Octave Convolution to the backbone network, reduces the number of parameters in the model, and improves computing efficiency. Betti and Tucci 8 introduced YOLO-S, a model that utilizes a compact feature extractor and combines low-level location information with more semantically rich high-level information through skip connections and connected pathways.…”

Section: Small Object Detectionmentioning

confidence: 99%

Normalization attention and lightweight convolution-based network for traffic sign detection

Zhao

2024

Fourth International Conference on Telecommunications, Optics, and Computer Science (TOCS 2023)

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Object detection and image recognition are considered to be the most critical and challenging aspects of computer vision. The rapid advancement of driverless technology and assisted driving systems further emphasizes the importance of traffic sign detection and recognition. Detecting small objects against complex backgrounds in practical assisted driving tasks remains a formidable challenge. Many existing target detection methods struggle to strike a balance between performance and parameters. In response, we propose a novel traffic sign detection model based on YOLOv8s-A Traffic Sign Detection Network incorporating Normalization-based Attention and Lightweight Convolution (YOLOv8-NL). The objective is to optimize the performance and address the challenge of balancing model parameters in traffic sign recognition within the transportation domain. Firstly, Normalization-based Attention (NAM) is introduced to address issues related to inconsistent input dimensions, uneven target distribution, and complex background of optimization. Secondly, lightweight Global Sparse Convolution (GSConv) is integrated to reduce the model parameters and enhance model generalization performance. Finally, to further enhance detection accuracy, the Wise-IOUv3 loss function is introduced to tackle difficulties associated with low-quality labeling of data. The effectiveness of the novel YOLOv8 model, which integrates Normalization-based Attention and Lightweight Convolution, is demonstrated through experiments conducted on two publicly available datasets. These experiments showcase the model's ability to significantly reduce parameters without compromising its guaranteed performance. It is noteworthy to note that significant improvements were observed in the CCTSDB2021 dataset, with a reduction of 1.2 million model parameters, resulting in 1% increase in mean average precision (mAP) compared to the YOLOv8s model. Furthermore, the mAP for the three-class target detection task on the TT00K dataset reached an impressive 94.2%.

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“…Reference [32] proposed an enhanced YOLOV4-Tiny traffic sign recognition algorithm, which solved the difficulties of the existing models, such as a high number of parameters, slow detection speed, and low recognition accuracy. By combining the YOLOV4-Tiny backbone network with Octave Convolution and adding the Convolutional Block Attention Module (CBAM), they improved the accuracy of traffic sign recognition.…”

Section: Traffic Sign 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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Improved traffic sign recognition algorithm based on YOLOv4-tiny

Cited by 47 publications

References 17 publications

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Developing a Portable Fluorescence Imaging Device for Fish Freshness Detection

Normalization attention and lightweight convolution-based network for traffic sign detection

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

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