TY - GEN
T1 - Small traffic sign detection and recognition in high-resolution images
AU - You, Lei
AU - Ke, Yu
AU - Wang, Hongpeng
AU - You, Wenhu
AU - Wu, Bo
AU - Song, Xinghao
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - Traffic sign detection and recognition is a research hotspot in the computer vision and intelligent transportation systems fields. It plays an important role in driver-assistance systems and driverless operation. Detecting signs, especially small ones, remains challenging under a variety of road traffic conditions. In this manuscript, we propose an end-to-end deep learning model for detecting and recognizing traffic signs in high-resolution images. The model consists of basic feature extraction and multi-task learning. In the first part, a network with fewer parameters is proposed, and an effective feature fusion strategy is adopted to gain a more distinct representation. In the second part, multi-task learning is conducted on different hierarchical layers by considering the difference between the detection and classification tasks. The detection results on two newly published traffic sign benchmarks (Tsinghua-Tencent 100K and CTSD) demonstrate the robustness and superiority of our model.
AB - Traffic sign detection and recognition is a research hotspot in the computer vision and intelligent transportation systems fields. It plays an important role in driver-assistance systems and driverless operation. Detecting signs, especially small ones, remains challenging under a variety of road traffic conditions. In this manuscript, we propose an end-to-end deep learning model for detecting and recognizing traffic signs in high-resolution images. The model consists of basic feature extraction and multi-task learning. In the first part, a network with fewer parameters is proposed, and an effective feature fusion strategy is adopted to gain a more distinct representation. In the second part, multi-task learning is conducted on different hierarchical layers by considering the difference between the detection and classification tasks. The detection results on two newly published traffic sign benchmarks (Tsinghua-Tencent 100K and CTSD) demonstrate the robustness and superiority of our model.
KW - End-to-end detection and recognition
KW - Small traffic sign detection
KW - Traffic sign detection
KW - Traffic sign recognition
UR - https://www.scopus.com/pages/publications/85068217121
U2 - 10.1007/978-3-030-23407-2_4
DO - 10.1007/978-3-030-23407-2_4
M3 - 会议稿件
AN - SCOPUS:85068217121
SN - 9783030234065
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 37
EP - 53
BT - Cognitive Computing – ICCC 2019 - 3rd International Conference, Held as Part of the Services Conference Federation, SCF 2019, Proceedings
A2 - Xu, Ruifeng
A2 - Wang, Jianzong
A2 - Zhang, Liang-Jie
PB - Springer Verlag
T2 - 3rd International Conference on Cognitive Computing, ICCC 2019, held as part of the Services Conference Federation, SCF 2019
Y2 - 25 June 2019 through 30 June 2019
ER -