STC-YOLO: Small Object Detection Network for Traffic Signs in Complex Environments

Abstract

The detection of traffic signs is easily affected by changes in the weather, partial occlusion, and light intensity, which increases the number of potential safety hazards in practical applications of autonomous driving. To address this issue, a new traffic sign dataset, namely the enhanced Tsinghua-Tencent 100K (TT100K) dataset, was constructed, which includes the number of difficult samples generated using various data augmentation strategies such as fog, snow, noise, occlusion, and blur. Meanwhile, a small traffic sign detection network for complex environments based on the framework of YOLOv5 (STC-YOLO) was constructed to be suitable for complex scenes. In this network, the down-sampling multiple was adjusted, and a small object detection layer was adopted to obtain and transmit richer and more discriminative small object features. Then, a feature extraction module combining a convolutional neural network (CNN) and multi-head attention was designed to break the limitations of ordinary convolution extraction to obtain a larger receptive field. Finally, the normalized Gaussian Wasserstein distance (NWD) metric was introduced to make up for the sensitivity of the intersection over union (IoU) loss to the location deviation of tiny objects in the regression loss function. A more accurate size of the anchor boxes for small objects was achieved using the K-means++ clustering algorithm. Experiments on 45 types of sign detection results on the enhanced TT100K dataset showed that the STC-YOLO algorithm outperformed YOLOv5 by 9.3% in the mean average precision (mAP), and the performance of STC-YOLO was comparable with that of the state-of-the-art methods on the public TT100K dataset and CSUST Chinese Traffic Sign Detection Benchmark (CCTSDB2021) dataset.

Enhanced YOLO- and Wearable-Based Inspection System for Automotive Wire Harness Assembly

“…It reaches a frame rate of 45 on VOC2007, significantly surpassing Faster R-CNN, which only achieves a frame rate of 7. This efficiency has led to its extensive application across various scenarios [17][18][19]. Regarding enhanced single-stage target detection algorithms tailored for specific tasks, refs.…”

RSI-YOLO: Object Detection Method for Remote Sensing Images Based on Improved YOLO

“…This method improves the detection performance of small targets. Huaqing Lai et al [ 21 ] designed a feature extraction module combining convolutional neural networks (CNN) and multi-head attention to obtain a larger receptive field and proposed STC-YOLO. The normalised Gaussian Wasserstein distance (NWD) metric is also introduced to improve the sensitivity of the algorithm loss to the position deviation of small targets.…”

MFF-YOLO: An Accurate Model for Detecting Tunnel Defects Based on Multi-Scale Feature Fusion

“…For example, the R-CNN [ 18 , 19 , 20 , 21 , 22 ] family of algorithms has high detection accuracy but is computationally intensive and inefficient. In contrast, single-stage algorithms such as the SSD [ 23 , 24 , 25 , 26 , 27 ] and YOLO [ 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ] series can achieve significant detection speedups. Single-stage target detection algorithms have become preferred in industrial applications due to their ability to directly output information about the position and detection frame of the target to be detected.…”