Abstract
In the area of traffic sign detection (TSD) methods, deep learning has been implemented and achieves outstanding performance. The detection of a traffic sign, as it has a dual function in monitoring and directing the driver, is a big concern for driver support systems. A core feature of autonomous vehicle systems is the identification of the traffic sign. This article focuses on the prohibitive sign. The objective is to detect in real-time and reduce processing time considerably. In this study, we implement the spatial pyramid pooling (SPP) principle to boost Yolo V3’s backbone network for the extraction of functionality. Our work uses SPP for more comprehensive learning of multiscale object features. Then, perform a comparative investigation of Yolo V3 and Yolo V3 SPP across various scales to recognize the prohibitory sign. Comparisons with Yolo V3 SPP models reveal that their mean average precision (mAP) is higher than Yolo V3. Furthermore, the test accuracy findings indicate that the Yolo V3 SPP model performs better than Yolo V3 for different sizes.
Highlights
Traffic sign recognition (TSR) technologies are an essential feature of numerous real-world implementations, including Automated Driver Assistance Systems (ADAS) [1,2], autonomous driving, traffic control, driver welfare, and maintenance of the road network
This paper aims to eliminate the network fixed-size restriction, obtain the best features in max-pooling layers, and enhance You Only Look Once (Yolo) V3 performance and the layer is established by the spatial pyramid pooling (SPP) layer [25,26,27,28]
The trend is that the accuracy of Yolo V3 SPP grows along with the detection time
Summary
Traffic sign recognition (TSR) technologies are an essential feature of numerous real-world implementations, including Automated Driver Assistance Systems (ADAS) [1,2], autonomous driving, traffic control, driver welfare, and maintenance of the road network. Many researchers are currently working on this problem with popular computer vision algorithms [3]. Identification tasks [8,9,10]. Most studies centered on creating profound convolutional neural networks (CNN) to increase precision [11,12]. The reason that traffic signs are created to be different and recognizable, using basic types and standardized colors to their country-specific existence, suggests a limiting issue in their identification and recognition. A method that generalizes efficient identification is difficult to find [1]
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