Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

Liang, Tianjiao; Bao, Hong; Pan, Weiguo; Pan, Feng

doi:10.1155/2022/3825532

Cited by 64 publications

(32 citation statements)

References 39 publications

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“…Detection performance in traffic scenes is evaluated using the BCTSDB [39], KITTI [40], and COCO [41] datasets to evaluate the generalization ability. The KITTI dataset contains 7481 training images and 7518 test images, totaling 80,256 labeled objects with three categories (e.g., vehicle, pedestrian, and cyclist).…”

Section: Datasetsmentioning

confidence: 99%

DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

Liang

Bao

Pan

et al. 2022

Sensors

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Object detection plays a vital role in autonomous driving systems, and the accurate detection of surrounding objects can ensure the safe driving of vehicles. This paper proposes a category-assisted transformer object detector called DetectFormer for autonomous driving. The proposed object detector can achieve better accuracy compared with the baseline. Specifically, ClassDecoder is assisted by proposal categories and global information from the Global Extract Encoder (GEE) to improve the category sensitivity and detection performance. This fits the distribution of object categories in specific scene backgrounds and the connection between objects and the image context. Data augmentation is used to improve robustness and attention mechanism added in backbone network to extract channel-wise spatial features and direction information. The results obtained by benchmark experiment reveal that the proposed method can achieve higher real-time detection performance in traffic scenes compared with RetinaNet and FCOS. The proposed method achieved a detection performance of 97.6% and 91.4% in AP50 and AP75 on the BCTSDB dataset, respectively.

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Section: Datasetsmentioning

confidence: 99%

DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

Liang

Bao

Pan

et al. 2022

Sensors

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“…Detection performance in traffic scenes was evaluated using the BCTSDB [26] and KITTI [27] datasets. BCTSDB is a traffic sign collection that comprises 15,690 images and 25,243 annotations, with labels classified as prohibitory, required, or warning.…”

Section: Datasetmentioning

confidence: 99%

AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving

et al. 2022

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The anchor-free-based object detection method is a crucial part in an autonomous driving system because of its low computing cost. However, the under-fitting of positive samples and over-fitting of negative samples affect the detection performance. An aspect-aware anchor-free detector is proposed in this paper to address this problem. Specifically, it adds an aspect prediction head at the end of the detector, which can learn different distributions of aspect ratios between other objects. The sample definition method is improved to alleviate the problem of positive and negative sample imbalance. A loss function is designed to strengthen the learning weight of the center point of the network. The validation results show that the AP50 and AP75 of the proposed method are 97.3% and 93.4% on BCTSDB, and the average accuracies of the car, pedestrian, and cyclist are 92.7%, 77.4%, and 78.2% on KITTI, respectively. The comparison results demonstrate that the proposed algorithm is better than existing anchor-free methods.

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“…The improved deep learning algorithm achieves an average accuracy of 84.22% on established data set. Liang et al [ 21 ] proposed a sparse R-CNN that integrates coordinate attention block with ResNeSt, and better performance is obtained. Ahmed [ 22 ] proposed a new network to enhance traffic sign areas in images of complex environments and used CURE-TSD sets to evaluate the effectiveness of the method, achieving 91.1% accuracy and 70.71% recall rate.…”

Section: Introductionmentioning

confidence: 99%

Traffic Sign Detection Based on SSD Combined with Receptive Field Module and Path Aggregation Network

Liao

2022

Computational Intelligence and Neuroscience

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The traditional traffic sign detection algorithm can not deal with the application scenarios such as intelligent transportation system or advanced assisted driving environment, and it is difficult to meet the application requirements in detection accuracy and efficiency. Focusing on the above problems, this paper proposes a traffic sign detection algorithm based on Single Shot Multibox Detector (SSD) combined with Receptive Field Module (RFM) and Path Aggregation Network (PAN). The proposed algorithm is abbreviated to SSD-RP. The SSD-RP uses the RFM to improve the receptive field and semantics of the predicted feature maps, thus improving the detection performance of small traffic signs. At the same time, the path aggregation network is introduced to integrate multiscale features, which makes the abstract semantic information and rich detailed information shared among multiscale feature maps, enhances the discrimination ability of feature system, and improves the location and classification accuracy of traffic signs. Following that, the spatial pyramid pooling module is used to pool the shallow features and integrate them into the bottom-up information transmission path of the path aggregation network, thus continuing to supplement the fine-grained features for the feature system and further improve the detection performance. The experimental results on GTSDB and CCTSDB data sets show that SSD-RP has higher mean average precision (mAP) than traditional SSD algorithm and can better detect small traffic signs, which means that SSD-RP has higher detection precision. In addition, the experimental results also show that, compared with the common object detection algorithms such as Faster R-CNN, RetinaNet, and YOLOv3, the SSD-RP achieves a better balance between detection time and detection precision.

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Traffic Sign Detection via Improved Sparse R-CNN for Autonomous Vehicles

Cited by 64 publications

References 39 publications

DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

DetectFormer: Category-Assisted Transformer for Traffic Scene Object Detection

AspectNet: Aspect-Aware Anchor-Free Detector for Autonomous Driving

Traffic Sign Detection Based on SSD Combined with Receptive Field Module and Path Aggregation Network

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