Traffic Sign Recognition in Harsh Environment Using Attention Based Convolutional Pooling Neural Network

Chung, Jun Ho; Kim, Dong Won; Kang, Tae-Koo; Lim, Myo Taeg

doi:10.1007/s11063-020-10211-0

Cited by 17 publications

(11 citation statements)

References 37 publications

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“…Jin et al 21 worked on GTSDB 36 . Also, works like Chung et al 22 with better performance with higher efficiency have additional hardware requirement of 1.20 GB; as such, our proposed technique is highly optimized and efficient.…”

Section: Discussionmentioning

confidence: 99%

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Traffic sign recognition on Indian database using wavelet descriptors and convolutional neural network ensemble

Sanyal

Mohapatra

Dash

2022

Concurrency and Computation

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Traffic sign recognition (TSR) has been a rising and lucrative field for researchers during the last decades. The high improvement of ADAS (autonomic driving autonomous system) has led researchers worldwide to concentrate on the development of TSR systems. As such, a novel automated multiclass TSR system is proposed. The architecture uses wavelet descriptors to extract the high information density and the traffic signs' edges and curves. The LL band image is directly fed into the classifier to avoid normalization. Three classifiers, CNN, CNN ensemble, and LSTM, are deployed for recognition. The architecture is implemented on IRSDBv1.0, the first available Indian traffic sign database. The architecture is also implemented on the standard traffic sign database GTSRB to investigate its effectiveness. An efficiency of 71.57% and 96.76% are recorded on IRSDBv1.0, and GTSRB, respectively. A list of comparative results is also provided to prove the competence of the architecture. The reasons behind the difference in the achieved accuracy are also discussed.

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Section: Discussionmentioning

confidence: 99%

“…The technique was named MF‐SSD (multiple feature single shot detector). Chung et al 22 worked on CNN for harsh environment. The author used an attention mechanism to extract robust descriptors and replaced convolution pooling by max‐pooling to improve recognition accuracy.…”

Section: Methodsmentioning

confidence: 99%

Traffic sign recognition on Indian database using wavelet descriptors and convolutional neural network ensemble

Sanyal

Mohapatra

Dash

2022

Concurrency and Computation

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“…According to the size of the signs, we divided the included traffic signs into small, medium, and large, as shown in Table 1. The ice and snow environment is not only just a representative environment, but also has a strong influence on the brightness, completeness, color saturation, and other information of the picture, which have been proved to have a great impact on traffic sign detection and classification [15,29]. The dataset includes these types of pictures and pictures of day and night, shown in Figure 1, which are more challenging for TSR problem not included in most public dataset like GTSRB.…”

Section: Itsdbmentioning

confidence: 99%

“…Different from [24,29], in the PFAN-A structure (shown in Figure 4a), the query, value, and key are the same (such as C 1 , C 2 . .…”

Section: Attention Modulementioning

confidence: 99%

Learning Region-Based Attention Network for Traffic Sign Recognition

Zhou

Zhan

2021

Sensors

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Traffic sign recognition in poor environments has always been a challenge in self-driving. Although a few works have achieved good results in the field of traffic sign recognition, there is currently a lack of traffic sign benchmarks containing many complex factors and a robust network. In this paper, we propose an ice environment traffic sign recognition benchmark (ITSRB) and detection benchmark (ITSDB), marked in the COCO2017 format. The benchmarks include 5806 images with 43,290 traffic sign instances with different climate, light, time, and occlusion conditions. Second, we tested the robustness of the Libra-RCNN and HRNetv2p on the ITSDB compared with Faster-RCNN. The Libra-RCNN performed well and proved that our ITSDB dataset did increase the challenge in this task. Third, we propose an attention network based on high-resolution traffic sign classification (PFANet), and conduct ablation research on the design parallel fusion attention module. Experiments show that our representation reached 93.57% accuracy in ITSRB, and performed as well as the newest and most effective networks in the German traffic sign recognition dataset (GTSRB).

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“…Unfortunately, the adverse environmental conditions, or the malfunctions of the camera, may produce low-quality frames that may negatively impact the performance of classifiers. Examples include, but are not limited to: occlusions, shadows, defects of the camera lens, changes in environmental light, raindrops on the camera lens, out-offocus, flare [18], [19]. Therefore, to guarantee safety of the driving task, it is necessary to study the robustness of TSR systems against the aforementioned threats, and develop solutions to tolerate them [20], [21].…”

Section: Introductionmentioning

confidence: 99%

Robust Traffic Sign Recognition Against Camera Failures

Atif

Ceccarelli

Zoppi

et al. 2022

IEEE Open J. Intell. Transp. Syst.

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Failures of the vehicle camera may compromise the correct acquisition of frames, that are subsequently used by autonomous driving tasks. A clear understanding of the behavior of the autonomous driving tasks under such failure conditions, together with strategies to avoid safety is jeopardized, are indeed necessary. This study analyses and improve the performance of Traffic Sign Recognition (TSR) systems for road vehicles under the possible occurrence of camera failures. Our experimental assessment relies on three public datasets, which are commonly used for benchmarking TSR systems. We artificially inject 13 different types of camera failures into the three datasets. Then, we exploit three deep neural networks (DNNs) to classify either a single frame of a traffic sign or a sequence (i.e., a sliding window) of frames. We show that sliding windows significantly improves the robustness of the classifier against altered frames. We confirm our observations through explainable AI, which allows understanding why different classifiers have different performance in case of camera failures.

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Traffic Sign Recognition in Harsh Environment Using Attention Based Convolutional Pooling Neural Network

Cited by 17 publications

References 37 publications

Traffic sign recognition on Indian database using wavelet descriptors and convolutional neural network ensemble

Traffic sign recognition on Indian database using wavelet descriptors and convolutional neural network ensemble

Learning Region-Based Attention Network for Traffic Sign Recognition

Robust Traffic Sign Recognition Against Camera Failures

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