1 |
BISWAS S K, MILANFAR P. Linear support tensor machine with LSK channels: pedestrian detection in thermal infrared images[J]. IEEE Transactions on Image Processing, 2017, 26(9): 4229-4242.
|
2 |
LIU J, ZHANG S, WANG S, et al. Multispectral deep neural networks for pedestrian detection[J]. arXiv preprint arXiv:, 2016.
|
3 |
李晓艳, 符惠桐, 牛文涛, 等. 基于深度学习的多模态行人检测算法[J]. 西安交通大学学报, 2022, 56(10): 61-70.
|
|
LI X Y, FU H T, NIU W T, et al. Muti-modal pedestrian detection algorithm based on deep learning[J]. Journal of Xi’an Jiaotong University, 2022, 56(10): 61-70.
|
4 |
KONIG D, ADAM M, JARVERS C, et al. Fully convolutional region proposal networks for multispectral person detection[C]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, USA: IEEE, 2017: 49-56.
|
5 |
邹伟, 殷国栋, 刘昊吉, 等. 基于多模态特征融合的自主驾驶车辆低辨识目标检测方法[J]. 中国机械工程, 2021, 32(9): 1114-1125.
|
|
ZOU W, YIN G, LIU H J, et al. Low-observable target detection method for autonomous vehicles based on multi-modal feature fusion[J]. China Mechanical Engineering, 2021, 32(9): 1114-1125.
|
6 |
ZHANG L, LIU Z, ZHANG S, et al. Cross-modality interactive attention network for multispectral pedestrian detection[J]. Information Fusion, 2019, 50: 20-29.
|
7 |
刘子龙, 沈祥飞. 融合Lite-HRNet的Yolo v5 双模态自动驾驶小目标检测方法[J]. 汽车工程, 2022, 44(10): 1511-1520.
|
|
LIU Z L, SHEN X F. Small target detection method for dual-modal autonomous driving with Yolo v5 and Lite-HRNet fusion[J]. Automotive Engineering, 2022, 44(10): 1511-1520.
|
8 |
FU L, GU W, AI Y, et al. Adaptive spatial pixel-level feature fusion network for multispectral pedestrian detection[J]. Infrared Physics & Technology, 2021, 116: 103770.
|
9 |
吴建国, 邵婷, 刘政怡. 融合显著深度特征的RGB-D图像显著目标检测[J]. 电子与信息学报, 2017, 39(9): 2148-2154.
|
|
WU J G, SHAO T, LIU Z Y. RGB-D saliency detection based on integration feature of color and depth saliency map[J]. Journal of Electronics & Information Technology, 2017, 39(9): 2148-2154.
|
10 |
CHEN S, TAN X, WANG B, et al. Reverse attention for salient object detection[C]. Proceedings of the European Conference on Computer Vision. Heidelberg: Springer, 2018: 234-250.
|
11 |
WANG W, ZHAO S, SHEN J, et al. Salient object detection with pyramid attention and salient edges[C]. Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 1448-1457.
|
12 |
刘迪,郭继昌,汪昱东,等. 融合注意力机制的多尺度显著性目标检测网络[J].西安电子科技大学学报, 2022, 49(4): 118-126.
|
|
LIU D, GUO J C, WANG Y D, et al. Multi-scale salient object detection network combining an attention mechanism[J]. Journal of Xidian University, 2022, 49(4): 118-126.
|
13 |
许小伟, 陈乾坤, 钱枫, 等. 基于小型化YOLOv3的实时车辆检测及跟踪算法[J]. 公路交通科技, 2020, 37(8): 149-158.
|
|
XU X W, CHEN Q K, QIAN F, et al. REAL-time vehicle detection and tracking based on miniaturized YOLOv3[J]. Journal of Highway and Transportation Research and Development, 2020, 37(8): 149-158.
|
14 |
杜虓龙, 余华平. 基于改进Mobile Net-SSD网络的驾驶员分心行为检测[J]. 公路交通科技, 2022, 39(3): 160-166.
|
|
DU X L, YU H P. Detecting driver’s distracted behavior based on improved mobile Net-SSD network[J]. Journal of Highway and Transportation Research and Development, 2022, 39(3): 160-166.
|
15 |
SOBEL I, FELDMAN G. A 3x3 isotropic gradient operator for image processing[J]. A Talk at the Stanford Artificial Project in, 1968: 271-272.
|
16 |
CANNY J. A computational approach to edge detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986 (6): 679-698.
|
17 |
WANG J S, REN X D. GLCM based extraction of flame image texture features and KPCA-GLVQ recognition method for rotary kiln combustion working conditions[J]. International Journal of Automation and Computing, 2014, 11(1): 72-77.
|
18 |
HAN K, WANG Y, TIAN Q, et al. Ghostnet: more features from cheap pperations[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Virtual:IEEE, 2020: 1580-1589.
|
19 |
REZATOFIGHI H, TSOI N, GWAK J Y, et al. Generalized intersection over union: a metric and a loss for bounding box regression[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach: IEEE, 2019: 658-666.
|
20 |
ZHENG Z, WANG P, LIU W, et al. Distance-IoU loss: faster and better learning for bounding box regression[C]. Proceedings of the AAAI Conference on Artificial Intelligence, New York: AAAI, 2020, 34(7): 12993-13000.
|