Faster-RCNN in human detecting on thermal images
Tác giả
Tài liệu tham khảo
[1] Messina, G., Modica, G.: Applications of UAV thermal imagery in precision agriculture: State of the art and future research outlook. Remote Sensing 12(9), 1491 (2020)
[2] Santamouris, M., Yun, G.Y.: Recent development and research priorities on cool and super cool materials to mitigate urban heat island. Renewable Energy 161, 792–807 (2020)
[3] He, Y., et al.: Infrared machine vision and infrared thermography with deep learning: A review. Infrared Phys. Technol. 116, 103754 (2021)
[4] Li, C., Xia, W., Yan, Y., Luo, B., Tang, J.: Segmenting objects in day and night: Edge-conditioned CNN for thermal image semantic segmentation. IEEE Trans. Neural Netw. Learn. Sys. 32(7), 3069–3082 (2020)
[5] Zhang, X., Qiu, Z., Huang, P., Hu, J., Luo, J.: Application research of YOLO v2 combined with color identification. In: 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), pp. 138–1383. IEEE (2018)
[6] Diwan, T., Anirudh, G., Tembhurne, J.V.: Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimedia Tools and Applications 82(6), 9243–9275 (2023)
[7] Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)
[8] Sahin, M.E., Ulutas, H., Yuce, E., Erkoc, M.F.: Detection and classification of COVID-19 by using faster R-CNN and mask R-CNN on CT images. Neural Comput. Appl. 35(18), 13597–13611 (2023)
[9] Qu, J., Su, C., Zhang, Z., Razi, A.: Dilated convolution and feature fusion SSD network for small object detection in remote sensing images. IEEE Access 8, 82832–82843 (2020)
[10] Manssor, S.A., Sun, S., Abdalmajed, M., Ali, S.: Real-time human detection in thermal infrared imaging at night using enhanced Tiny-yolov3 network. J. Real-Time Image Proc. 19(2), 261–274 (2022)
[11] Vijayakumar, A., Vairavasundaram, S.: Yolo-based object detection models: A review and its applications. Multimedia Tools and Applications, 1–40 (2024)
[12] Tao, C., et al.: An efficient 3D object detection method based on fast guided anchor stereo RCNN. Adv. Eng. Inform. 57, 102069 (2023)
[13] Xin, F., Zhang, H., Pan, H.: Hybrid dilated multilayer faster RCNN for object detection. Vis. Comput. 40(1), 393–406 (2024)
[14] Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
[15] Shuai, Q., Wu, X.: Object detection system based on SSD algorithm. In: 2020 international conference on culture-oriented science & technology (ICCST), pp. 141–144. IEEE (2020)
[16] Padilla, R., Netto, S.L., Da Silva, E.A.: A survey on performance metrics for object-detection algorithms. In: 2020 international conference on systems, signals and image processing (IWSSIP), pp. 237–242. IEEE (2020)