This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Viola, P., Jones, M., Rapid object detection using a boosted cascade of simple features., Proceedings of the 2001 IEEE computer society conference on computer vision and pattern recognition, 2001, 511-518.Search in Google Scholar
Dalal, N., Triggs, B., Histograms of oriented gradients for human detection., IEEE computer society conference on computer vision and pattern recognition, 2005, 886-893.Search in Google Scholar
He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition., Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 770-778.Search in Google Scholar
Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K., Aggregated residual transformations for deep neural networks., Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 1492-1500.Search in Google Scholar
Ren, S., He, K., Girshick, R., Sun, J., Faster r-cnn: Towards real-time object detection with region proposal networks., Advances in neural information processing systems, 2015.Search in Google Scholar
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S., Feature pyramid networks for object detection., Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 2117-2125.Search in Google Scholar
Lin, T. Y., Goyal, P., Girshick, R., He, K., Dollár, P., Focal loss for dense object detection., Proceedings of the IEEE international conference on computer vision, 2017, 2980-2988.Search in Google Scholar
Tian, Z., Shen, C., Chen, H., He, T., FCOS: A simple and strong anchor-free object detector., IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020, 1922-1933.Search in Google Scholar
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S., End-to-end object detection with transformers., European conference on computer vision, 2020, 213-229.Search in Google Scholar
Tao, M., Li, X., Ota, K., Dong, M., Single-Cell Multiuser Computation Offloading in Dynamic Pricing-Aided Mobile Edge Computing., IEEE Transactions on Computational Social Systems, 2023.Search in Google Scholar
Cheng, G., Han, J., A survey on object detection in optical remote sensing images., ISPRS journal of photogrammetry and remote sensing, 2016, 11-28.Search in Google Scholar
Geronimo, D., Lopez, A. M., Sappa, A. D., Graf, T., Survey of pedestrian detection for advanced driver assistance systems., IEEE transactions on pattern analysis and machine intelligence, 2009, 1239-1258.Search in Google Scholar
Jensen, M. B., Philipsen, M. P., Møgelmose, A., Moeslund, T. B., Trivedi, M., Vision for looking at traffic lights: Issues, survey, and perspectives., IEEE transactions on intelligent transportation systems, 2016, 1800-1815.Search in Google Scholar
Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Zitnick, C. L., Microsoft coco: Common objects in context., Proceedings of the European conference on computer vision (ECCV), 2014, 740-755.Search in Google Scholar
Xu, S., Wang, X., Lv, W., Chang, Q., Cui, C., Deng, K., Lai, B., PP-YOLOE: An evolved version of YOLO., arXiv preprint arXiv:2203.16250.Search in Google Scholar
Yang, C., Huang, Z., Wang, N., QueryDet: Cascaded sparse query for accelerating high-resolution small object detection., Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, 2022, 13668-13677.Search in Google Scholar
He, K., Zhang, X., Ren, S., Sun, J., Deep residual learning for image recognition., Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 770-778.Search in Google Scholar
Li, Y., Chen, Y., Wang, N., Zhang, Z., Scale-aware trident networks for object detection., Proceedings of the IEEE/CVF international conference on computer vision, 2019, 6054-6063.Search in Google Scholar
Chen, L., Zheng, H., Yan, Z., Li, Y., Discriminative region mining for object detection., IEEE Transactions on Multimedia, 2020, 4297-4310.Search in Google Scholar
Li, J., Liang, X., Wei, Y., Xu, T., Feng, J., Yan, S., Perceptual generative adversarial networks for small object detection., Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 1222-1230.Search in Google Scholar
Cai, Z., Fan, Q., Feris, R. S., Vasconcelos, N., A unified multi-scale deep convolutional neural network for fast object detection., Proceedings of the European conference on computer vision (ECCV), 2016, 354-370.Search in Google Scholar
Lin, T. Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S., Feature pyramid networks for object detection., Proceedings of the IEEE conference on computer vision and pattern recognition, 2017, 2117-2125.Search in Google Scholar
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C. Y., Berg, A. C., Ssd: Single shot multibox detector., Proceedings of the European conference on computer vision (ECCV), 2016, 21-37.Search in Google Scholar
Lou, H., Duan, X., Guo, J., Liu, H., Gu, J., Bi, L., Chen, H., DC-YOLOv8: small-size object detection algorithm based on camera sensor., Electronics, 12(10), 2323.Search in Google Scholar
Yu, W., Zhou, P., Yan, S., Wang, X., Inceptionnext: When inception meets convnext., arXiv preprint arXiv:2303.16900.Search in Google Scholar
Zhu, P., Wen, L., Du, D., Bian, X., Ling, H., Hu, Q., Song, Z., Visdrone-det2018: The vision meets drone object detection in image challenge results., Proceedings of the European Conference on Computer Vision (ECCV) Workshops, 2018.Search in Google Scholar
Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., Tian, Q., The unmanned aerial vehicle benchmark: Object detection and tracking., Proceedings of the European conference on computer vision (ECCV), 2018, 370-386.Search in Google Scholar
Puertas, E., De-Las-Heras, G., Fernández-Andrés, J., Sánchez-Soriano, J., Dataset: Roundabout Aerial Images for Vehicle Detection., Data, 2022, 47.Search in Google Scholar
Girshick, R., Donahue, J., Darrell, T., Malik, J., Rich feature hierarchies for accurate object detection and semantic segmentation., Proceedings of the IEEE conference on computer vision and pattern recognition, 2014, 580-587.Search in Google Scholar
He, K., Zhang, X., Ren, S., Sun, J., Spatial pyramid pooling in deep convolutional networks for visual recognition., IEEE transactions on pattern analysis and machine intelligence, 2015, 1904-1916.Search in Google Scholar
GIRSHICK, Ross., Fast r-cnn., Proceedings of the IEEE international conference on computer vision, 2015, 1440-1448.Search in Google Scholar
Redmon, J., Divvala, S., Girshick, R., Farhadi, A., You only look once: Unified, real-time object detection., Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, 779-788.Search in Google Scholar
Zhang, Z., Drone-YOLO: an efficient neural network method for target detection in drone images., Drones, 7(8), 526.Search in Google Scholar
He, K., Gkioxari, G., Dollár, P., Girshick, R., Mask r-cnn., Proceedings of the IEEE international conference on computer vision, 2017, 2961-2969.Search in Google Scholar
Dai, J., Li, Y., He, K., Sun, J., R-fcn: Object detection via region-based fully convolutional networks., Advances in neural information processing systems, 2016, 29.Search in Google Scholar
Redmon, J., Farhadi, A., Yolov3: An incremental improvement., arXiv preprint arXiv:1804.02767.Search in Google Scholar
Bochkovskiy, A., Wang, C. Y., Liao, H. Y. M., Yolov4: Optimal speed and accuracy of object detection., arXiv preprint arXiv:2004.10934.Search in Google Scholar
Tian, Z., Shen, C., Chen, H., He, T., Fcos: Fully convolutional one-stage object detection., Proceedings of the IEEE/CVF international conference on computer vision, 2019, 9627-9636.Search in Google Scholar
Uzkent, B., Yeh, C., Ermon, S., Efficient object detection in large images using deep reinforcement learning., Proceedings of the IEEE/CVF winter conference on applications of computer vision, 2020, 1824-1833.Search in Google Scholar
Kong, T., Sun, F., Liu, H., Jiang, Y., Li, L., Shi, J., Foveabox: Beyound anchor-based object detection., IEEE Transactions on Image Processing, 2020, 7389-7398.Search in Google Scholar
Law, H., Deng, J., Cornernet: Detecting objects as paired keypoints., Proceedings of the European conference on computer vision (ECCV), 2018, 734-750.Search in Google Scholar
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J., Yolox: Exceeding yolo series in 2021., arXiv preprint arXiv:2107.08430.Search in Google Scholar
Lee, Y., Park, J., Centermask: Real-time anchor-free instance segmentation., Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, 13906-13915.Search in Google Scholar
Feng, C., Zhong, Y., Gao, Y., Scott, M. R., Huang, W., Tood: Task-aligned one-stage object detection., In 2021 IEEE/CVF International Conference on Computer Vision (ICCV), 2021, 3490-3499.Search in Google Scholar
Zoph, B., Cubuk, E. D., Ghiasi, G., Lin, T. Y., Shlens, J., Le, Q. V., Learning data augmentation strategies for object detection., Proceedings of the European conference on computer vision (ECCV), 2020, 566-583.Search in Google Scholar
Yu, F., Koltun, V., Multi-scale context aggregation by dilated convolutions., arXiv preprint arXiv:1511.07122.Search in Google Scholar
Chen, L. C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A. L., Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs., IEEE transactions on pattern analysis and machine intelligence, 2017, 834-848.Search in Google Scholar
He, K., Zhang, X., Ren, S., Sun, J., Identity mappings in deep residual networks., Proceedings of the European conference on computer vision (ECCV), 2016, 630-645.Search in Google Scholar
Yu, G., Chang, Q., Lv, W., Xu, C., Cui, C., Ji, W., Ma, Y., PP-PicoDet: A better real-time object detector on mobile devices., arXiv preprint arXiv:2111.00902.Search in Google Scholar
Rezatofighi, H., Tsoi, N., Gwak, J., Sadeghian, A., Reid, I., Savarese, S., Generalized intersection over union: A metric and a loss for bounding box regression., In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2019, 658-666.Search in Google Scholar
Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Yang, J., Generalized focal loss: Learning qualified and distributed bounding boxes for dense object detection., Advances in Neural Information Processing Systems, 33, 2020, 21002-21012.Search in Google Scholar