[1] Sun Y C, Pan S G, Zhao T et al. Traffic light detection based on optimized YOLOv3 algorithm[J]. Acta Optica Sinica, 40, 1215001(2020).

[2] Xue F F, Wang Y M, Li Q. Recognition of cattle daily behavior based on spatial relationship of feature parts[J]. Laser & Optoelectronics Progress, 58, 2215007(2021).

[3] Nong Y J, Wang J J. Real-time object detection in remote sensing images based on embedded system[J]. Acta Optica Sinica, 41, 1028001(2021).

[4] Law H, Deng J. CornerNet: detecting objects as paired keypoints[J]. International Journal of Computer Vision, 128, 642-656(2020).

[5] Zhu C C, He Y H, Savvides M. Feature selective anchor-free module for single-shot object detection[C], 840-849(2019).

[6] Tian Z, Shen C H, Chen H et al. FCOS: fully convolutional one-stage object detection[C], 9626-9635(2019).

[7] Zhou X Y, Zhuo J C, Krähenbühl P. Bottom-up object detection by grouping extreme and center points[C], 850-859(2019).

[8] Duan K W, Bai S, Xie L X et al. CenterNet: keypoint triplets for object detection[C], 6568-6577(2019).

[9] Kong T, Sun F C, Liu H P et al. FoveaBox: beyound anchor-based object detection[J]. IEEE Transactions on Image Processing, 29, 7389-7398(2020).

[10] Zhang S F, Chi C, Yao Y Q et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[C], 9756-9765(2020).

[11] Cai Z W, Vasconcelos N. Cascade R-CNN: delving into high quality object detection[C], 6154-6162(2018).

[12] Li A, Yang X, Zhang C Y. Rethinking classification and localization for cascade R-CNN[EB/OL]. https://arxiv.org/abs/1907.11914

[13] Zhang S F, Wen L Y, Bian X et al. Single-shot refinement neural network for object detection[C], 4203-4212(2018).

[14] Yang Z, Liu S H, Hu H et al. RepPoints: point set representation for object detection[C], 9656-9665(2019).

[15] Yang X, Yan J, Feng Z et al. R3Det: refined single-stage detector with feature refinement for rotating object[C], 35, 3163-3171.

[16] Kong T, Sun F C, Liu H P et al. Consistent optimization for single-shot object detection[EB/OL]. https://arxiv.org/abs/1901.06563v2

[17] Lin T Y, Maire M, Belongie S J et al. Microsoft COCO: common objects in context[M]. Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science, 8693, 740-755(2014).

[18] Everingham M, Gool L, Williams C K I et al. The pascal visual object classes (VOC) challenge[J]. International Journal of Computer Vision, 88, 303-338(2010).

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

[20] Chen K, Wang J Q, Pang J M et al. MMDetection: open MMLab detection toolbox and benchmark[EB/OL]. https://arxiv.org/abs/1906.07155v1

[21] Duan K W, Xie L X, Qi H G et al. Location-sensitive visual recognition with cross-IOU loss[EB/OL]. https://arxiv.org/abs/2104.04899

[22] Lin T Y, Goyal P, Girshick R et al. Focal loss for dense object detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 318-327(2020).

[23] Chen Y H, Emer J, Sze V. Eyeriss: a spatial architecture for energy-efficient dataflow for convolutional neural networks[C], 367-379(2016).

[24] Ma N N, Zhang X Y, Zheng H T et al. ShuffleNet V2: practical guidelines for efficient CNN architecture design[M]. Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science, 11218, 122-138(2018).