[1] [1] ERDAW H B, TAYE Y G, LEMMA D T. A real-time obstacle detection and classification system for assisting blind and visually impaired people based on Yolo model[C]//2023 International Conference on Information and Communication Technology for Development for Africa (ICT4DA). New York, USA: IEEE Press, 2023: 79-84.

[2] [2] DUMAN S, ELEWI A, YETGIN Z. Design and implementation of an embedded real-time system for guiding visually impaired individuals[C]//2019 International Artificial Intelligence and Data Processing Symposium (IDAP). New York, USA: IEEE Press, 2019: 1-5.

[3] [3] ESPINACE P, KOLLAR T, SOTO A,et al. Indoor scene recognition through object detection[C]//2010 IEEE International Conference on Robotics and Automation. New York, USA: IEEE Press, 2010: 1406-1413.

[4] [4] KIM J, LEE C H, YOUNGC,et al. Optical sensor based object detection for autonomous robots[C]//20118th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI). New York, USA: IEEE Press, 2011: 746-754.

[6] [6] GIRSHICK R, DONAHUE J, DARRELL T,et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2014: 580-587.

[7] [7] REDMON J, DIVVALA S, GIRSHICK R,et al. You only look once: unified, real-time object detection[C]//Procee Dings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2016: 779-788.

[8] [8] WU T H, WANG T W, LIU Y Q. Real-time vehicle and distance detection based on improved yolov5 network[C]//2021 3rd World Symposium on Artificial Intelligence (WSAI). New York, USA: IEEE Press, 2021: 24-28.

[9] [9] JIANG L, NIE W, ZHU J,et al. Lightweight object detection network model suitable for indoor mobile robots[J]. Journal of Mechanical Science and Technology, 2022, 36(2): 907-920.

[10] [10] LIU W, ANGUELOV D, ERHAN D,et al. SSD: Single shot multibox detector[C]//Computer Vision-ECCV 2016: 14th European Conference. New York, USA: Springer Press, 2016: 21-37.

[12] [12] MA N, ZHANG X, ZHENG H T,et al. Shufflenetv2: Practical guidelines for efficient CNN architecture design[C]//Proceedings of the European conference on computer vision (ECCV). New York, USA: IEEE Press, 2018: 116-131.

[14] [14] REDMON J, FARHADI A. Yolov3: An incremental improvement[C]//IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2018: 45-48.

[16] [16] TANG H, LIANG S, YAO D,et al. A visual defect detection for optics lens based on the YOLOv5-C3CA-SPPF network model[J]. Optics Express, 2023, 31(2): 2628-2643.

[17] [17] WANG C Y, BOCHKOVSKIY A, LIAO H Y M. YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2023: 7464-7475.

[18] [18] HAN K, WANG Y, TIAN Q,et al. Ghostnet: More features from cheap operations[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2020: 1580-1589.

[19] [19] LOU H T, DUAN X H, GUO J M,et al. DC-YOLOv8: Small-size object detection algorithm based on camera sensor[J]. Electronics, 2023, 12(10): 2323.

[20] [20] LIU Y, SHAO Z, HOFFMANN N. Global attention mechanism: Retain information to enhance channel-spatial interactions[EB/OL]. (2021-12-10) [2024-01-16]. https://arxiv.org/abs/2112.05561.

[21] [21] DAI J, QI H, XIONG Y,et al. Deformable convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision. New York, USA: IEEE Press, 2017: 764-773.

[22] [22] ZHANG X Y, ZHOU X Y, LIN M X. ShuffleNet: an extremely effificient convolutional neural network for mobile devices[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2018: 00716.

[23] [23] HOWARD A G, ZHU M, CHEN B,et al. Mobilenets: Efficient convolutional neural networks for mobile vision applications[EB/OL]. (2017-04-17) [2024-01-26]. https://arxiv.org/abs/1704.04861.

[24] [24] HE K, ZHANG X, REN S,et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2016: 770-778.

[25] [25] HE K, ZHANG X, REN S,et al. Spatial pyramid pooling in deep convolutional networks for visual recognition[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(9): 1904-1916.

[28] [28] SILBERMAN N, HOIEM D, KOHLI P,et al. Indoor segmentation and support inference from rgbd images[C]//Computer Vision-ECCV 2012: 12th European Conference on Computer Vision. New York, USA: Springer Press, 2012: 746-760.

[29] [29] QUATTONI A, TORRALBA A. Recognizing indoor scenes[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2009: 413-420.

[30] [30] XIAO J, HAYS J, EHINGER K A,et al. Sun database: Large-scale scene recognition from abbey to zoo[C]//2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2010: 3485-3492.

[31] [31] LIN T Y, MAIRE M, BELONGIE S,et al. Microsoft COCO: Common objects in context[C]//Computer Vision-ECCV 2014: 13th European Conference. New York, USA: Springer Press, 2014: 740-755.

[32] [32] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2018: 7132-7141.

[33] [33] LIU Y, SHAO Z, TENG Y,et al. NAM: Normalization-based attention module[EB/OL]. (2021-11-24) [2024-01-26]. https://arxiv.org/abs/2111.12419.

[34] [34] HOU Q, ZHOU D, FENG J. Coordinate attention for efficient mobile network design[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2021: 13713-13722.

[35] [35] SRINIVAS A, LIN T Y, PARMAR N,et al. Bottleneck transformers for visual recognition[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE Press, 2021: 16519-16529.

[36] [36] WOO S Y, PARK J C, LEE J Y,et al. CBAM: Convolutional block attention module[C]//Proceedings of the European Conference on Computer Vision (ECCV). New York, USA: Springer Press, 2018: 3-19.

[37] [37] 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, 2017, 39(6): 1137-1149.