Traffic-Sign Detection and Classification in the Wild

By Zhe Zhu, Dun Liang, Song-Hai Zhang, Xiaolei Huang, Baoli Li, Shimin Hu. Computer Vision and Pattern Recognition, 2016

882 citations recorded by the source corpus.

Subfields: Civil and Structural Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/cdd8bad29b5e90a1f92080eaca51ba123f34ada5

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Civil and Structural Engineering

In Computer Vision and Pattern Recognition

Citations in the corpus

VPGNet: Vanishing Point Guided Network for Lane and Road Marking Detection and Recognition (2017, methodology): “Originally in [16], [35], the box regression task aims to fit a single box to a particular object.”
Towards Large-Scale Small Object Detection: Survey and Benchmarks (2022, methodology): “TT100 K: TT100K [153] is a dataset for realistic trafﬁc sign detection which includes 30000 trafﬁc sign instances in 100000 images, covering 45 common Chinese trafﬁc-sign classes.”
Extended Feature Pyramid Network for Small Object Detection (2020, methodology): “[40] 82.”
Vision-based vehicle detection and counting system using deep learning in highway scenes (2019, background): “The Tsinghua-Tencent TrafficSign Dataset [32] has 100,000 images from car cameras covering various lighting conditions and weather conditions, although no vehicles are marked.”
Deep Learning for Generic Object Detection: A Survey (2018, background): “Since our focus is on generic object detection, the extensive work on DCNNs for specific object detection, such as face detection [154, 306, 116], pedestrian detection [307, 109], vehicle detection [322] and traffic sign detection [329] will not be considered.”
Object Detection in 20 Years: A Survey (2019, methodology): “TT100K [83] 2016 The largest traffic sign detection dataset so far, with ∼100,000 images (2048 x 2048) and ∼30,000 traffic sign instances of 128 classes.”
Metric Learning for Novelty and Anomaly Detection (2018, methodology): “We compare our proposed method ODM against ODIN [17], as a cross-entropy based method, on the Tsinghua dataset [33].”
No-Reference Image Quality Assessment via Transformers, Relative Ranking, and Self-Consistency (2021, background): “On the other hand, at one extreme, not being able to assess the image quality accurately can be life-threatening ( e.g., when low-quality images impede the ability of autonomous vehicles [1, 2] and traffic controllers [3] to safely navigate environments).”
Real-Time Object Detection With Reduced Region Proposal Network via Multi-Feature Concatenation (2020, background): “Prune [1–5] in the green region denotes pruning in convolution layers while prune [6 and 7] in the blue region represents pruning in FC layers.”
Recent Advancements in End-to-End Autonomous Driving Using Deep Learning: A Survey (2023, neutral): “Tsinghua-Tencent 100k [129] 2016 ✓ ✓ ✓ 30k China CC-BY-NC”
Spatial Attentive Single-Image Deraining With a High Quality Real Rain Dataset (2019, background): “, pedestrian detection [30], visual tracking [37], or road sign recognition [48].”
Multimodal End-to-End Autonomous Driving (2019, background): “Perception itself is already especially complex, since it involves sub-tasks such as object detection [5–10] and tracking [11–15], traffic sign recognition [16],”
Perceptual Generative Adversarial Networks for Small Object Detection (2017, methodology): “[45] in terms of average recall and accuracy: 89% and 84% vs 87% and 82%, 96% and 91% vs 94% and 91%, 89% and 91% vs 88% and 91% on three subsets of different object sizes.”
Deep Multi-Modal Object Detection and Semantic Segmentation for Autonomous Driving: Datasets, Methods, and Challenges (2019, background): “Deep learning approaches have set the benchmark on many popular object detection datasets, such as PASCAL VOC [17] and COCO [18], and have been widely applied in autonomous driving, including detecting traffic lights [19]–[22], road signs [23]–[25], people [26]–[28],”
Empowering Things With Intelligence: A Survey of the Progress, Challenges, and Opportunities in Artificial Intelligence of Things (2020, background): “The self-driving system should perceive the driving environment, such as detecting road [85], traffic sign [259], pedestrian [260], and”
Recent Advances in Deep Learning for Object Detection (2019, background): “There are also many other real-world applications based on object detection such as vehicle detection [227, 228, 229], traffic-sign detection [230, 231] and skeleton detection [232, 233].”
Improved YOLOv5 network for real-time multi-scale traffic sign detection (2021, methodology): “The mAP of our method on the TT100K dataset is 4.96% higher than that of the YOLOv5s, which means the proposed method achieves impressive performance in target and recognition.”