Traffic sign detection algorithm based on feature expression enhancement

Sun, Chao; Wen, Mei; Zhang, Kai; Meng, Ping; Cui, Rongcheng

doi:10.1007/s11042-021-11413-x

Cited by 7 publications

(2 citation statements)

References 21 publications

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“…The comparison of STC-YOLO with state-of-theart methods on the CCTSDB2021 dataset is shown in Table 6. It can be seen that STC-YOLO outperformed the one-stage methods ESSD [37], YOLOv3 + MAF + SIA [49], M-YOLO [38], and YOLOv5s in the mAP. Compared with the two-stage method Faster R-CNN + ACFPN + Auto Augment [50], STC-YOLO achieved a comparable mAP and outstanding speed for real detection.…”

Section: Performance On the Tt100k Datasetmentioning

confidence: 95%

“…Zhang et al [36] used the Cascade R-CNN [8] combined with the sample balance method to detect traffic signs, achieving ideal detection results on both CCTSDB and GTSDB. Sun et al [37] proposed a feature expression enhanced SSD detection algorithm, which achieved an 81.26% and 90.52% mAP on TT100K and CCTSDB, respectively. However, the detection speed of this algorithm was only 22.86 FPS and 25.08 FPS, which could not achieve real-time performance.…”

Section: Traffic Signs Detectionmentioning

confidence: 99%

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

Lai

Chen

Liu

et al. 2023

Sensors

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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.

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