Multi-scale traffic sign detection model with attention

Fan, Bei Bei; Yang, He

doi:10.1177/0954407020950054

Cited by 18 publications

(4 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The method [35][36][37] only considers the difference of feature weights on the channel, so ignores the feature weights on the space. Fan et al [38] focused on the spatial feature information and used the spatial attention mechanism to improve the detection performance of the detector for traffic signs. CBAM [11] pays attention to both channel and spatial feature information to make the extracted features more refined.…”

Section: Attention Mechanismmentioning

confidence: 99%

Attention feature fusion network for small traffic sign detection

Wu¹,

Yang²,

Zhang³

et al. 2022

Eng. Res. Express

View full text Add to dashboard Cite

Object detection has made great progress with the rise of convolutional neural networks in recent years. Traffic sign detection is a research hotspot for object detection tasks. The existing detection models have the problems of inaccurate positioning and low classification accuracy when detecting small traffic signs. To address these issues, in this paper, we propose a small traffic sign detection method based on YOLOv4. Specifically, we design an attention-based feature fusion module including attention spatial pyramid pooling (ASPP) and attention path aggregation networks (APAN). ASPP highlights useful small object information and suppresses invalid interference information in the background. APAN reduces information loss during feature fusion. A large number of experimental results on public datasets show that the method in this paper improves the detection performance of the model. In terms of small traffic sign detection, the method improves YOLOv4 by 12 mAP, and meets the real-time requirements of automatic driving detection (more than 50 FPS).

show abstract

Section: Attention Mechanismmentioning

confidence: 99%

Attention feature fusion network for small traffic sign detection

Wu¹,

Yang²,

Zhang³

et al. 2022

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Addressing these issues, the current study presents a framework for TSR complete with a testbed for validating the system's accuracy and latency [16]. A computing unit and server were installed in a vehicle to allow the system to recognize traffic signs in real-world conditions.…”

Section: Introductionmentioning

confidence: 99%

Stacked Extreme Learning Machine with Horse Herd Optimization: A Methodology for Traffic Sign Recognition in Advanced Driver Assistance Systems

Jayapal,

Muvva,

Desanamukula

2023

MITS

View full text Add to dashboard Cite

In the quest for autonomous vehicle safety and road infrastructure management, traffic sign recognition (TSR) remains paramount. Recent advancements in accuracy across various benchmarks have been identified in the literature concerning this essential task. Such technology might remain absent in older vehicles, while integration into Advanced Driver Assistance Systems (ADAS) is common in more recent models. Yet, the capability of these systems to function proficiently under diverse driving conditions has not been widely investigated. A framework has been devised to allow a moving vehicle to detect traffic signs, targeting the enhancement of driver safety and the diminishment of accidents. The present research introduces an innovative methodology, amalgamating the extreme learning machine (ELM) method with deep-learning paradigms, in response to experimental discoveries. As a pioneering computational approach in neural network-based learning, ELM facilitates rapid training and commendable generalization. An accuracy of 95.00% was achieved by the proposed model. By utilizing the Horse Herd Optimization method (HHOA), the memory consumption is minimized in the more sophisticated approach of stacked ELM (SELM) within the deep-learning framework. This study contributes to the understanding of potential challenges that may be encountered during TSR tasks, and lays the groundwork for future investigation by proffering a diverse set of evaluations for various road scenarios. Consistency in the utilization of professional terms is maintained throughout.

show abstract

“…Mainly, there are two types of traffic signs i.e. (1) American with white background and black foreground and (2) European having either red rim or filled with blue color [2,3,4,5]. In this work, we focus on European traffic signs only.…”

Section: Introductionmentioning

confidence: 99%

Cognition based Recognition of Partially Occluded Traffic Signs

et al. 2022

View full text Add to dashboard Cite

Computer vision based traffic sign detection and recognition is an active field of research but the task becomes challenging when the sign of interest is partially occluded by nearby objects like a tree, pole or vehicle. Another difficulty posed especially in the developing countries is the lost colors problem that arises due to aging and poor maintenance. This work presents an automatic technique that focuses on visible parts only and suppresses occluded portions. Features are collected using a convolutional neural network inspired invariant feature extraction technique augmented with feature interaction based dimensionality reduction. Further, with the use of dynamic parameter estimation, an adaptive system for continuous learning is also proposed. Since the effect of partial occlusion has not been thoroughly studied, there is no benchmark database available for this purpose. We have prepared two datasets by combining originally and synthetically occluded images taken from field surveys and from famous GTSRB database. Experiments revealed that our technique outperforms state of the art recognition methods previously used for visible and occluded signs by obtaining 0.81 precision and 0.79 recall values on the average. The proposed method also shows a remarkably low error rate as the amount of occlusion is increased.

show abstract

Multi-scale traffic sign detection model with attention

Cited by 18 publications

References 34 publications

Attention feature fusion network for small traffic sign detection

Attention feature fusion network for small traffic sign detection

Stacked Extreme Learning Machine with Horse Herd Optimization: A Methodology for Traffic Sign Recognition in Advanced Driver Assistance Systems

Cognition based Recognition of Partially Occluded Traffic Signs

Contact Info

Product

Resources

About