Optics and Precision Engineering, Volume. 30, Issue 13, 1620(2022)
Relocation non-maximum suppression algorithm
[1] WU X W, SAHOO D, HOI S C H. Recent advances in deep learning for object detection[J]. Neurocomputing, 396, 39-64(2020).
[2] SUN Y N, XUE B, ZHANG M J et al. Automatically designing CNN architectures using the genetic algorithm for image classification[J]. IEEE Transactions on Cybernetics, 50, 3840-3854(2020).
[3] LIU Y, WU Y H, WEN P S et al. Leveraging instance-, image- and dataset-level information for weakly supervised instance segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44, 1415-1428(2022).
[4] CHENG Q M, ZHANG Q, FU P et al. A survey and analysis on automatic image annotation[J]. Pattern Recognition, 79, 242-259(2018).
[5] CIAPARRONE G, LUQUE SÁNCHEZ F, TABIK S et al. Deep learning in video multi-object tracking: a survey[J]. Neurocomputing, 381, 61-88(2020).
[6] GIRSHICK R, DONAHUE J, DARRELL T et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. OH, 580-587(2014).
[7] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 60, 84-90(2017).
[8] WAN S H, GOUDOS S. Faster R-CNN for multi-class fruit detection using a robotic vision system[J]. Computer Networks, 168, 107036(2020).
[9] WANG N, GAO Y, CHEN H et al. NAS-FCOS: efficient search for object detection architectures[J]. International Journal of Computer Vision, 129, 3299-3312(2021).
[10] [10] 10王建林, 付雪松, 黄展超, 等. 改进YOLOv2卷积神经网络的多类型合作目标检测[J]. 光学 精密工程, 2020, 28(1): 251-260. doi: 10.3788/ope.20202801.0251WANGJ L, FUX S, HUANGZH CH, et al. Multi-type cooperative targets detection using improved YOLOv2 convolutional neural network[J]. Optics and Precision Engineering, 2020, 28(1): 251-260.(in Chinese). doi: 10.3788/ope.20202801.0251
[11] [11] 11鞠默然, 罗海波, 刘广琦, 等. 采用空间注意力机制的红外弱小目标检测网络[J]. 光学 精密工程, 2021, 29(4): 843-853. doi: 10.37188/OPE.20212904.0843JUM R, LUOH B, LIUG Q, et al. Infrared dim and small target detection network based on spatial attention mechanism[J]. Optics and Precision Engineering, 2021, 29(4): 843-853.(in Chinese). doi: 10.37188/OPE.20212904.0843
[12] [12] 12马立, 巩笑天, 欧阳航空. Tiny YOLOV3目标检测改进[J]. 光学 精密工程, 2020, 28(4): 988-995.MAL, GONGX T, OUYANGH K. Improvement of Tiny YOLOV3 target detection[J]. Optics and Precision Engineering, 2020, 28(4): 988-995.(in Chinese)
[13] NEUBECK A, LVAN GOOL. Efficient non-maximum suppression[C], 850-855(2006).
[14] QIU S H, WEN G J, DENG Z P et al. Accurate non-maximum suppression for object detection in high-resolution remote sensing images[J]. Remote Sensing Letters, 9, 237-246(2018).
[15] HE Y H, ZHU C C, WANG J R et al. Bounding box regression with uncertainty for accurate object detection[C], 2883-2892(2019).
[16] MA W C, LI K D, WANG G H. Location-aware box reasoning for anchor-based single-shot object detection[J]. IEEE Access, 129300-129309(8).
[17] Jiang B, Luo R, Mao J et al. Acquisition of Localization Confidence for Accurate Object Detection[C], 784-799(2018).
[18] ZENG J X, XIONG J L, FU X et al. ReFPN-FCOS: one-stage object detection for feature learning and accurate localization[J]. IEEE Access, 225052-225063(8).
[19] LIU S T, HUANG D, WANG Y H. Adaptive NMS: refining pedestrian detection in a crowd[C], 6452-6461(2019).
[20] ZHENG Z H, WANG P, LIU W et al. Distance-IoU loss: faster and better learning for bounding box regression[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 34, 12993-13000(2020).
[21] BODLA N, SINGH B, CHELLAPPA R et al. Soft-NMS-improving object detection with one line of code[C], 5562-5570(2017).
Get Citation
Copy Citation Text
Shuzhi SU, Runbin CHEN, Yanmin ZHU, Bowen JIANG. Relocation non-maximum suppression algorithm[J]. Optics and Precision Engineering, 2022, 30(13): 1620
Category: Information Sciences
Received: Dec. 24, 2021
Accepted: --
Published Online: Jul. 27, 2022
The Author Email: Shuzhi SU (sushuzhi@foxmail.com)