Abstract
In computer vision tasks, better performance of the transformer model is due to self-attention mechanism and learning of global information. However, it also largely increases parameters and calculations. In this paper, we propose the following problems. (1) How to build a lighter module that integrates CNN and Transformer? We propose the ML-block module in this paper. Especially, for one thing, reducing the number of channels after the convolution module; for another, spatial attention is introduced after the ML-block input layer to reduce the loss caused by information fusion. (2) Small object detection problem in one-stage object detector. We propose weighted IoU loss in this paper. According to the object size, it adaptively weighs the IoU loss to improve the performance of the detector. We adopt YOLOX-s as the baseline through sufficient experiments on PASCAL VOC data sets and COCOmini data sets to demonstrate the effectiveness of our methods, and AP increases by 1.1\(\%\) and 3.3\(\%\).
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References
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Chen, Y., et al.: Dynamic scale training for object detection. arXiv preprint arXiv:2004.12432 (2020)
Ge, Z., Liu, S., Wang, F., Li, Z., Sun, J.: Yolox: exceeding yolo series in 2021. arXiv preprint arXiv:2107.08430 (2021)
Ghiasi, G., Lin, T.Y., Pang, R., Le, Q.V.: NAS-FPN: learning scalable feature pyramid architecture for object detection. In: CVPR (2019)
Glenn-Jocher, R., et al.: yolov5 (2021). https://github.com/ultralytics/yolov5
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask R-CNN. In: ICCV (2017)
Li, Y., Yao, T., Pan, Y., Mei, T.: Contextual transformer networks for visual recognition. arXiv preprint arXiv:2107.12292 (2021)
Lim, J.S., Astrid, M., Yoon, H.J., Lee, S.I.: Small object detection using context and attention. In: 2021 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), pp. 181–186. IEEE (2021)
Lin, T.Y., Dollár, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR (2017)
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: ICCV (2017)
Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48
Liu, S., Qi, L., Qin, H., Shi, J., Jia, J.: Path aggregation network for instance segmentation. In: CVPR (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Mehta, S., Rastegari, M.: Mobilevit: Light-weight, general-purpose, and mobile-friendly vision transformer. arXiv preprint arXiv:2110.02178 (2021)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: CVPR (2016)
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: CVPR (2017)
Redmon, J., Farhadi, A.: Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767 (2018)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NeurIPS (2015)
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, pp. 658–666 (2019)
Samet, N., Hicsonmez, S., Akbas, E.: HoughNet: integrating near and long-range evidence for bottom-up object detection. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12370, pp. 406–423. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58595-2_25
Tang, Y., Gu, B., Jiang, B.: Research on feature enhancement for small object detection. In: Journal of Physics: Conference Series, vol. 2006, p. 012036. IOP Publishing (2021)
Wang, Q., Zhang, H., Hong, X., Zhou, Q.: Small object detection based on modified FSSD and model compression. arXiv preprint arXiv:2108.10503 (2021)
Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3–19. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01234-2_1
Yu, J., Jiang, Y., Wang, Z., Cao, Z., Huang, T.: Unitbox: an advanced object detection network. In: Proceedings of the 24th ACM international conference on Multimedia, pp. 516–520 (2016)
Zhang, H., Hong, X., Zhu, L.: Detecting small objects in thermal images using single-shot detector. Autom. Control Comput. Sci. 55(2), 202–211 (2021)
Zheng, Z., Wang, P., Liu, W., Li, J., Ye, R., Ren, D.: Distance-IOU loss: faster and better learning for bounding box regression. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 12993–13000 (2020)
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Yuan, Z., Xiao, X., Zhao, S., Jiang, L., Li, Z. (2022). Effective ML-Block and Weighted IoU Loss for Object Detection. In: Pimenidis, E., Angelov, P., Jayne, C., Papaleonidas, A., Aydin, M. (eds) Artificial Neural Networks and Machine Learning – ICANN 2022. ICANN 2022. Lecture Notes in Computer Science, vol 13532. Springer, Cham. https://doi.org/10.1007/978-3-031-15937-4_19
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