Robust traffic sign detection and recognition (TSDR) is of paramount importance for the successful realization autonomous vehicle technology. The this task has led to vast amount research efforts many promising methods have been proposed in existing literature. However, most these evaluated on clean challenge-free datasets overlooked performance deterioration associated with different challengi...

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In this study, we propose a novel traffic sign detection algorithm based on the deeplearning approach. The proposed algorithm, which termed feature-selection-based attentionaldeconvolution detector (FSADD), is used along with “you look only once” (YOLO) v5 structure for feature selection. When applying selection inside network divides extracted maps after convolution layer into similar and non ...

Most existing traffic sign detection models suffer from high computational complexity and superior performance but cannot be deployed on edge devices with limited capacity, which meet the direct needs of autonomous vehicles for model efficiency. To address above concerns, this paper proposes an improved SEDG-Yolov5 method based knowledge distillation. Firstly, Slicing Aided Hyper Inference is u...

Recent developments in autonomous driving have created a great demand for precise and computationallyeffective traffic sign-detecting systems. By assisting drivers assuring their safety, such technology canlessen accidents fatalities. However, to make system deployable, few crucial accuracyand processing performance problems must be resolved. Real-time is sometimes regarded as amust an applicat...

Accurate Traffic Sign Detection (TSD) can help drivers make better decision according to the traffic regulations. TSD, regarded as a typical small object detection problem in some way, is fundamental in the field of self-driving and advanced driver assistance systems. However, small object detection is still an open question. In this paper, we proposed a human brain inspired network to handle t...