###### Abstract  
Traffic sign recognition is a key module of autonomous cars and driver assistance systems. Traffic sign detection accuracy and inference time are the two most important parameters. Current methods for traffic sign recognition are very accurate; however, they do not meet the requirement for real-time detection. While some are fast enough for real-time traffic sign detection, they fall short in accuracy. This paper proposes an accuracy improvement in the YOLOv3 network, which is a very fast detection framework. The proposed method contributes to the accurate detection of a small-sized traffic sign in terms of image size and helps to reduce false positives and miss rates. In addition, we propose an anchor frame selection algorithm that helps in achieving the optimal size and scale of the anchor frame. Therefore, the proposed method supports the detection of a small traffic sign with real-time detection. This ultimately helps to achieve an optimal balance between accuracy and inference time. The proposed network is evaluated on two publicly available datasets, namely the German Traffic Sign Detection Benchmark (GTSDB) and the Swedish Traffic Sign dataset (STS), and its performance showed that the proposed approach achieves a decent balance between mAP and inference time. YOLOv3; traffic sign detection; small objects detection; anchor box selection +
Footnote †: journal: Medical  
Vast research material pertaining to this field is available and researchers are able to achieve high precision and recall values. For example, [PERSON] [1] and [PERSON] [2] reported near 100% traffic sign detection accuracy on the German Traffic Sign Detection Benchmark (GTSDB) [3]. However, its real-time recognition with resource-limited constraints in a real scenario is still a challenging task. Classical object feature extraction methods, such as gradients, color, and texture use contour, color, and texture features to locate traffic signs. Nonetheless, these features in the current demand are not promising. Such features change with illumination conditions, and the presence of similar shapes or color objects in the background can cause false detection. Hence, in today's era, researchers are focused on convolutional neural network (CNN) based features. CNN-based detectors include Region Proposals networks such as Fast R-CNN [4], Faster R-CNN [5] and Mask R-CNN [6]. There are also single-pass image-based CNN detectors such as Single Shot Multibox Detector [7] and YOLO [8]. Region Proposal-based CNN detectors achieve higher accuracy but are slow in real-time bounding boxes prediction. Single-pass image detectors provide better inference time, but are error-prone and yield less accuracy. For an autonomous car, inference time is as equally crucial as accuracy; this gives more reaction time, to make timely and appropriate decisions to prevent fatal accidents. A 100% accurate method is useless if it is not able to detect traffic signs in due time. Similarly, a real-time detector with limited accuracy has no importance. Thus, researchers are working to achieve optimal values for accuracy and inference time. This will make traffic sign detection faster and precise. In this paper, we propose an improved single image pass CNN-based YOLOv3 network framework for real-time traffic sign recognition, which is more accurate and faster at detecting traffic signs than the state-of-the-art methods. The main contributions of this paper focus on the framework of the YOLOv3 algorithm and are summarized as follows:  
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* Tweaked YOLOv3 model for smaller object detection: YOLOv3 model uses multiple DBL layers for object detection. For larger object size relative to image size, these DBL layers are sufficient however, for a smaller object such as a traffic sign, useless features are being learned. We propose pruning a few of those layers and validating the rationale on GTSDB. This improvement helped in extraction and saving fine details of traffic signs. In addition, a new strategy for training and testing is proposed.