Remote Sensing Technology and Application, Volume. 39, Issue 3, 590(2024)
Object Detection in Remote Sensing Images based on YOLOX-Tiny Biased Feature Fusion Network
[1] LI Shutao, LI Congyu, KANG Xudong. Development status and future prospects of multi-source remote sensing image fusion. National Remote Sensing Bulletin, 25, 148-166(2021).
[2] TAN Qulin, SHAO Yun. Application of remote sensing technology to environment pollution monitoring. Remote Sensing Technology and Application, 15, 246-251(2000).
[3] GIRSHICK R, DONAHUE J, DARRELL T et al. Rich feature hierarchies for accurate object detection and semantic segmentation, 580-587(2014).
[4] GIRSHICK R. Fast R-CNN, 1440-1448(2015).
[5] REN S, HE K, GIRSHICK R et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 1137-1149(2017).
[6] REDMON J, DIVVALA S, GIRSHICK R et al. You only look once: Unified, real-time object detection, 779-788(2016).
[7] REDMON J, FARHADI A. YOLOV3:An incremental improvement, 1-6(2018).
[8] [8] BOCHKOVSKIYA, WANGC Y, LIAOH. YOLOv4: optimal speed and accuracy of object detection[S].arXiv Preprint arXiv: 2020, 2004.10934. DOI:10.48550/arXiv.2004.10934.
[9] LIU W, ANGUELOV D, ERHAN D et al. SSD: Singleshot multibox detector, 21-37(2016).
[10] LIN Y, GOYAL P, GIRSHICK R. Focal loss for dense object detection, 2980-2988(2017).
[11] DUAN K W, BAI S, XIE L X et al. CenterNet: Keypoint triplets for object detection, 6568-6577(2019).
[12] ZHANG W, WANG S H, THACHAN S et al. Deconv R-CNN for small object detection on remote sensing images, 2483-2486(2018).
[13] YANG X, SUN H, SUN X et al. Position detection and direction prediction for arbitrary-oriented ships via multitask rotation region convolutional neural network. IEEE Access, 6, 50839-50849(2018).
[14] ZHU X K, WANG X, ZHAO Q et al. TPH-YOLOv5: Improved YOLOv5 based on transformer prediction head for object detection on drone-captured scenarios, 2778-2788(2022).
[15] YU H M, XU F X. A remote sensing image target recognition method based on improved Mask-RCNN model, 436-439(2021).
[16] LIN T Y, DOLLAR P, GIRSHICK R et al. Feature pyramid networks for object detection, 2117-2125(2017).
[17] [17] QIAOS, CHENL C, YUILLEA.DetectoRS: Detecting objects with recursive feature pyramid and switchable atrous convolution[S].arXiv preprint arXiv: 2020, 2006.02334. 2021: 10213-10224. DOI:10.48550/arXiv.2106.02334
[18] ZHANG S, HE G H, CHEN H B et al. Scale adaptive proposal network for object detection in remote sensing images. IEEE Geoscience and Remote Sensing Letters, 16, 864-868(2019).
[19] ZHANG G J, LU S J, ZHANG W. CAD-Net: A context-aware detection network for objects in remote sensing imagery. IEEE Transactions on Geoscience and Remote Sensing, 57, 10015-10024(2019).
[20] [20] GEZ,LIUS,WANGF,et al.YOLOX: exceeding YOLO series in 2021[S].arXiv preprint arXiv:2021,2107.08430. DOI:10.48550/arXiv.2107.08430
[21] LIU S, QI L, QIN H et al. Path aggregation network for instance segmentation, 8759-8768(2018).
[22] WANG G X, LIU Z Q, SUN H et al. Yolox-BTFPN: An anchor-free conveyor belt damage detector with a biased feature extraction network. Measurement, 200, 111675(2022).
[23] TAN M X, PANG R M, LE Q V. EfficientDet: Scalable and efficient object detection, 10778-10787(2020).
[24] DAI J, QI H, XIONG Y et al. Deformable convolutional networks, 764-773(2017).
[25] [25] GEVORGYANZ. SIoU Loss: More powerful learning for bounding box regression[S].arXiv Preprint arXiv:2022,2205. 12740. DOI:10.48550/arXiv.2205.12740
[26] LI K, WAN G, CHENG G et al. Object detection in optical remotesensing images: A survey and a new benchmark. ISPRS Journal of Photogrammetry and Remote Sensing, 159, 296-307(2020).
[28] [28] WANGC Y, BOCHKOVSKIYA, LIAOH Y M. YOLOv7:Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[S]. arXiv preprint arXiv:2022,2207.02696. DOI:10.48550/arXiv.2207.02696
[29] XIAO Z F, LIU Q, TANG G F et al. Elliptic Fourier transformation-based histograms of oriented gradients for rotationally invariant object detection in remote-sensing images. International Journal of Remote Sensing, 36, 618-644(2015).
[30] TIAN Tingting, YANG Jun. Remote sensing image object detection based on multi-scale feature fusion network. Laser & Optoelectronics Progress, 59, 427-435(2022).
[31] WANG Peng, ZHENG Wenfeng, SHI Jin et al. Object detection of remote sensing image based on MFANet and context feature fusion. Journal of Applied Sciences, 40, 131-144(2022).
[32] MA Qiaomei, WANG Mingjun, LIANG Haoran. License plate location detection algorithm based on improved YOLOv3 in complex scenes. Computer Engineering and Applications, 57, 198-208(2021).
Get Citation
Copy Citation Text
Zhaohua HU, Yuhui LI. Object Detection in Remote Sensing Images based on YOLOX-Tiny Biased Feature Fusion Network[J]. Remote Sensing Technology and Application, 2024, 39(3): 590
Category:
Received: Nov. 22, 2022
Accepted: --
Published Online: Dec. 9, 2024
The Author Email: