Attention feature fusion network for small traffic sign detection

Wu, Miaozhi; Yang, Jingmin; Zhang, Wenjie; Zheng, Yuan; Liao, Jianxin

doi:10.1088/2631-8695/ac8de1

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…In manufacturing, for example, DL models have the capability to extract meaningful information from imprecise sensory input, thereby contributing to the development of intelligent production systems. One of the key advantages of deep learning in comparison to classical machine learning is its ability to autonomously perform feature learning without the need for external involvement [14].…”

Section: Related Workmentioning

confidence: 99%

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Saleh,

Metin ERTUNÇ

2024

Eng. Res. Express

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In the tire manufacturing industry, ensuring the quality of tires is of utmost importance because defective tires have the potential to fail explosively, particularly in high-speed driving situations such as races. To address this issue, thorough visual inspections after production are essential. Nevertheless, detecting defects in tires is a challenging task due to the various textures and structures they have. This paper introduces an Explainable Attention-based Fused Convolutional Neural Network (XAFCNN) model for tire defect detection. Special Attention Module (SAM) is used to prevent overfitting and improve local feature mapping, enabling the detection of small objects. The Grad-CAM heatmap method is utilized to offer significant visual indicators that enrich our comprehension of how the suggested model analyzes data and discerns relevant features within the tire samples. The model was trained on a dataset comprising 38,710 X-ray images of defective tires and 83,985 defect-free tire images, encompassing 15 defect types across 50 design patterns. The results demonstrate the model's exceptional performance, achieving a recall rate of 86.85%, a precision of 98.5%, an F1 score of 92.31%, and an overall accuracy of 95.40%. Notably, the model excels in identifying diverse tire defects and complex textures, making it highly effective in real-world scenarios. By providing both a substantial dataset and a high-performing model, this research advances automated tire defect detection, meeting the industry's imperative for accurate and reliable inspections, and ultimately enhancing human safety.

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Section: Related Workmentioning

confidence: 99%

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Saleh,

Metin ERTUNÇ

2024

Eng. Res. Express

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Traffic sign detection algorithm based on YOLOv5 combined with BIFPN and attention mechanism

Zhang,

Mo,

Wan

2023

2023 IEEE 7th Information Technology and Mechatronics Engineering Conference (ITOEC)

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PSG-Yolov5: A Paradigm for Traffic Sign Detection and Recognition Algorithm Based on Deep Learning

Hu¹,

Wang²,

Chang³

et al. 2022

Symmetry

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With the gradual popularization of autonomous driving technology, how to obtain traffic sign information efficiently and accurately is very important for subsequent decision-making and planning tasks. Traffic sign detection and recognition (TSDR) algorithms include color-based, shape-based, and machine learning based. However, the algorithms mentioned above are insufficient for traffic sign detection tasks in complex environments. In this paper, we propose a traffic sign detection and recognition paradigm based on deep learning algorithms. First, to solve the problem of insufficient spatial information in high-level features of small traffic signs, the parallel deformable convolution module (PDCM) is proposed in this paper. PDCM adaptively acquires the corresponding receptive field preserving the integrity of the abstract information through symmetrical branches thereby improving the feature extraction capability. Simultaneously, we propose sub-pixel convolution attention module (SCAM) based on the attention mechanism to alleviate the influence of scale distribution. Distinguishing itself from other feature fusion, our proposed method can better focus on the information of scale distribution through the attention module. Eventually, we introduce GSConv to further reduce the computational complexity of our proposed algorithm, better satisfying industrial application. Experimental results demonstrate that our proposed methods can effectively improve performance, both in detection accuracy and mAP@0.5. Specifically, when the proposed PDCM, SCAM, and GSConv are applied to the Yolov5, it achieves 89.2% mAP@0.5 in TT100K, which exceeds the benchmark network by 4.9%.

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Attention feature fusion network for small traffic sign detection

Cited by 3 publications

References 38 publications

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Explainable attention-based fused convolutional neural network (XAFCNN) for tire defect detection: an industrial case study

Traffic sign detection algorithm based on YOLOv5 combined with BIFPN and attention mechanism

PSG-Yolov5: A Paradigm for Traffic Sign Detection and Recognition Algorithm Based on Deep Learning

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