Traffic Sign Detection and Recognition Using Multi-Scale Fusion and Prime Sample Attention

Cao, Jinghao; Zhang, Junju; Huang, Wei

doi:10.1109/access.2020.3047414

Cited by 22 publications

(9 citation statements)

References 34 publications

(39 reference statements)

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“…Domen et al [26] propose an improved mask R-CNN [27] to address the full pipeline of detection with end-to-end learning, which cannot detect small and multiscale traffic signs. Cao et al [28] improved faster R-CNN through the high-resolution backbone network [29] and prime sample strategy [30]. Xie et al [31] proposed improved cascade R-CNN [32] for traffic-sign detection.…”

Section: Related Workmentioning

confidence: 99%

“…Dense-to-sparse algorithms Domen et al [26] Mask R-CNN √ --Cao et al [28] Faster R-CNN -✓ ✓ Xie et al [31] Cascade R-CNN ✓ ✓ -Sparse algorithms Sun et al [6] Sparse R-CNN --the proposal box, ROI, and feature vector and detects each ROI separately without an NMS operation. us, the candidate boxes can be sparse, and the interactions between features can also be sparse.…”

Section: Dense Algorithmsmentioning

confidence: 99%

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Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

Liang

Bao

Pan

et al. 2022

Journal of Advanced Transportation

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Traffic sign detection is an important component of autonomous vehicles. There is still a mismatch problem between the existing detection algorithm and its practical application in real traffic scenes, which is mainly due to the detection accuracy and data acquisition. To tackle this problem, this study proposed an improved sparse R-CNN that integrates coordinate attention block with ResNeSt and builds a feature pyramid to modify the backbone, which enables the extracted features to focus on important information, and improves the detection accuracy. In order to obtain more diverse data, the augmentation method used is specifically designed for complex traffic scenarios, and we also present a traffic sign dataset in this study. For on-road autonomous vehicles, we designed two modules, self-adaption augmentation (SAA) and detection time augmentation (DTA), to improve the robustness of the detection algorithm. The evaluations on traffic sign datasets and on-road testing demonstrate the accuracy and effectiveness of the proposed method.

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Section: Related Workmentioning

confidence: 99%

Section: Dense Algorithmsmentioning

confidence: 99%

Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

Liang

Bao

Pan

et al. 2022

Journal of Advanced Transportation

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“…In response to this problem, Cao et al [28] improved Faster R-cnn through the HRNet(High-Resolution Network) [29] backbone network and PISA (Prime Sample) sample strategy [30]. Wang et al [31] used ResNet-101 to improve the backbone of Cascade R-cnn [32] for traffic-sign detection.…”

Section: Related Workmentioning

confidence: 99%

A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn

Cao¹,

Zhang

Jin

2021

IEEE Access

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Automatic traffic-sign detection is a hot topic in computer vision and one of the critical technologies of intelligent transportation. The Transformer structure has recently become a research hotspot due to its excellent performance. We hope to apply this structure to the design of traffic sign detection algorithms. Therefore, we make some improvements to Sparse R-cnn, a neural network model inspired by Transformer. Sparse R-cnn is a novel model, and its core idea is to replace hundreds of thousands of candidate anchors in the RPN network with a small set of proposal boxes. The experiments in our paper proved that the performance of the Sparse R-cnn model is better than other existing general object detection models. Based on the original Sparse R-cnn inspiration, an improved Sparse R-cnn model is proposed. First, a novel backbone for the task of traffic-sign detection is proposed. Multi-scale fusion structure is the essential method of improving the algorithm for small target detection, so improving the multi-scale capability of the backbone is a required method for designing traffic sign detection. So, we made further improvements to the existing backbone ResNest. We enhanced the multi-scale representation ability of the backbone by constructing hierarchical residual-like connections within each single radix block in the original ResNest. We call the improved backbone Res2Nest. The novel backbone proposed by us shows better performance without introducing excessive computational costs to the model. In addition, the attention mechanism is also an effective method to improve the detection of traffic signs, so we set up a branch network for recalibrating the channel feature response adaptively through the Global Average Pooling (GAP) operation and a fully connected layer. It can also be seen as the implementation of the crosschannel self-attention mechanism. After experiments by TT100K dataset, our method would attain a better accuracy and robustness.

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“…In this work, we focus on European traffic signs only. Many computer vision based methods to automatically detect and recognize European traffic signs have been reported in literature [1,6,7,8,9,10]. Detection serves the purpose of segmenting a traffic sign in a real world scene whereas recognition deals with reading its contents.…”

Section: Introductionmentioning

confidence: 99%

Cognition based Recognition of Partially Occluded Traffic Signs

et al. 2022

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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.

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Traffic Sign Detection and Recognition Using Multi-Scale Fusion and Prime Sample Attention

Cited by 22 publications

References 34 publications

Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

A Traffic-Sign Detection Algorithm Based on Improved Sparse R-cnn

Cognition based Recognition of Partially Occluded Traffic Signs

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