Indoor Scene Object Detection Based on Improved YOLOv4 Algorithm

Weigang Li; Chao Yang; Lin Jiang; Yuntao Zhao

doi:10.3788/LOP202259.1815003

[1] Zhang H J, Zhang C N, Yang W et al. Localization and navigation using QR code for mobile robot in indoor environment[C], 2501-2506(2015).

[2] Rafique A A, Jalal A, Kim K. Statistical multi-objects segmentation for indoor/outdoor scene detection and classification via depth images[C], 271-276(2020).

[3] Espinace P, Kollar T, Soto A et al. Indoor scene recognition through object detection[C], 1406-1413(2010).

[4] Kim J, Lee C H, Young-Chul et al. Optical sensor-based object detection for autonomous robots[C], 746-752(2011).

[5] Zhu S G, Du J P, Ren N. A novel simple visual tracking algorithm based on hashing and deep learning[J]. Chinese Journal of Electronics, 26, 1073-1078(2017).

[6] Luo H L, Chen H K. Survey of object detection based on deep learning[J]. Acta Electronica Sinica, 48, 1230-1239(2020).

[7] Zou B, Lin S Y, Yin Z S. Semantic mapping based on YOLOv3 and visual SLAM[J]. Laser & Optoelectronics Progress, 57, 201012(2020).

[8] Afif M, Ayachi R, Said Y et al. An evaluation of RetinaNet on indoor object detection for blind and visually impaired persons assistance navigation[J]. Neural Processing Letters, 51, 2265-2279(2020).

[9] Yao X Y. Research on indoor object detection based on deep learning[D](2018).

[10] Li W G, Ye X, Zhao Y T et al. Strip steel surface defect detection based on improved YOLOv3 algorithm[J]. Acta Electronica Sinica, 48, 1284-1292(2020).

[11] Ren S Q, He K M, Girshick R et al. Faster R-CNN: towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).

[12] He K M, Gkioxari G, Dollár P et al. Mask R-CNN[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 386-397(2020).

[13] Liu W, Anguelov D, Erhan D et al. SSD: single shot MultiBox detector[M]. Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science, 9905, 21-37(2015).

[14] Redmon J, Divvala S, Girshick R et al. You only look once: unified, real-time object detection[C], 779-788(2016).

[15] Redmon J, Farhadi A. YOLO9000: better, faster, stronger[C], 6517-6525(2017).

[16] Redmon J, Farhadi A. YOLOv3: an incremental improvement[EB/OL]. https://arxiv.org/abs/1804.02767

[17] Bochkovskiy A, Wang C Y, Liao H Y M. YOLOv4: optimal speed and accuracy of object detection[EB/OL]. https://arxiv.org/abs/2004.10934

[18] Wang C Y, Liao H Y M, Wu Y H et al. CSPNet: a new backbone that can enhance learning capability of CNN[C], 1571-1580(2020).

[19] He K M, Zhang X Y, Ren S Q et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 37, 1904-1916(2015).

[20] Liu S, Qi L, Qin H F et al. Path aggregation network for instance segmentation[C], 8759-8768(2018).

[21] Lin T Y, Dollár P, Girshick R et al. Feature pyramid networks for object detection[C], 936-944(2017).

[22] Wang C Y, Bochkovskiy A, Liao H Y M. Scaled-YOLOv4: scaling cross stage partial network[C], 13024-13033(2021).

[23] Zhang Y B, Guo W, Zhou Y et al. Real-time target detection of underwater relics based on multigranularity pruning[J]. Laser & Optoelectronics Progress, 58, 1410019(2021).

[24] Jacob B, Kligys S, Chen B et al. Quantization and training of neural networks for efficient integer-arithmetic-only inference[C], 2704-2713(2018).

[25] Hinton G, Vinyals O, Dean J. Distilling the knowledge in a neural network[EB/OL]. https://arxiv.org/abs/1503.02531

[26] Yu C D, Bi X J, Han Y et al. Particle image velocimetry based on a lightweight deep learning model[J]. Acta Optica Sinica, 40, 0720001(2020).

[27] Howard A G, Zhu M L, Chen B et al. MobileNets: efficient convolutional neural networks for mobile vision applications[EB/OL]. https://arxiv.org/abs/1704.04861