Abstract
In foggy object detection tasks, the presence of airborne particles reduces imaging clarity, resulting in a significant decrease in detection accuracy. Existing fog removal networks lack evaluation metrics for high-level tasks, and connecting the fog removal network limits the adaptability of the object detection network. To address these issues, this paper proposes training the fog removal network with a perceptual-loss approach involving the object detection network. This approach aims to enhance the accuracy of the fog removal network in advanced tasks and overcome the constraints of quantitative evaluation indexes like PSNR. We compare the results of training DefogNet with perceptual loss and pixel-level loss, and obtain the best results in terms of PSNR and SSM indices using both losses. Although the object detection network connected to the dehazing network can handle detection task in foggy scenes, its accuracy decreases in such scenarios. For this reason, we propose the DefogDA-FasterRCNN network, which incorporates domain adaptation into the integrated network, and makes the object detection module domain-adaptive for both foggy and non-foggy domains that pass through the dehazing module. Foggy images will obtain clearer features through the fog removal network and the negative impact of foggy images through the fog removal network will be weakened by the domain adaptation.
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References
Li, W., Li, F., Luo, Y., Wang, P., et al.: Deep domain adaptive object detection: a survey. In: SSCI, pp. 1808–1813. IEEE (2020)
Cai, B., Xu, X., Jia, K., Qing, C., Tao, D.: DehazeNet: an end-to-end system for single image haze removal. IEEE TIP 25(11), 5187–5198 (2016)
Li, B., Peng, X., Wang, Z., Xu, J., Feng, D.: An all-in-one network for dehazing and beyond. arXiv e-prints arXiv:1707.06543, July 2017
Ren, W., et al.: Gated fusion network for single image dehazing. In: CVPR, pp. 3253–3261. IEEE (2018)
Li, B., et al.: Benchmarking single image dehazing and beyond. IEEE TIP 28, 492–505 (2018)
Chen, Y., Li, W., Sakaridis, C., Dai, D., Van Gool, L.: Domain adaptive faster R-CNN for object detection in the wild. In: CVPR, pp. 3339–3348. IEEE (2018)
Zhu, X., Pang, J., Yang, C., Shi, J., Lin, D.: Adapting object detectors via selective cross-domain alignment. In: CVPR, pp. 687–696. IEEE (2019)
Shan, Y., Lu, W.F., Chew, C.M.: Pixel and feature level based domain adaptation for object detection in autonomous driving. Neurocomputing 367, 31–38 (2019)
Rodriguez, A.L., Mikolajczyk, K.: Domain adaptation for object detection via style consistency. arXiv preprint arXiv:1911.10033 (2019)
Zou, X., Liu, Y., Tan, Z.F., Tu, C.S., Zhang, H.: A fog-removing treatment based on combining high-frequency emphasis filtering and histogram equalization. KEM 474, 2198–2202 (2011)
Soni, B., Mathur, P.: An improved image dehazing technique using CLAHE and guided filter. In: SPIN, pp. 902–907 (2020)
He, Z., Patel, V.M.: Densely connected pyramid dehazing network. In: CVPR, pp. 3194–3203. IEEE (2018)
Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: CVPR, vol. 1, pp. 1–11. IEEE (2001)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, vol. 1, pp. 886–893. IEEE (2005)
Girshick, R., Donahue, J., Darrell, T., Malik, J.: Region-based convolutional networks for accurate object detection and segmentation. IEEE TPAMI 38(1), 142–158 (2015)
Girshick, R.: Fast R-CNN. In: ICCV, pp. 1440–1448. IEEE (2015)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: towards real-time object detection with region proposal networks. In: NeurIPS, vol. 28, pp. 91–99 (2015)
Lin, T.Y., Dollar, P., Girshick, R., He, K., Hariharan, B., Belongie, S.: Feature pyramid networks for object detection. In: CVPR, pp. 2117–2125. IEEE (2017)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: CVPR, pp. 779–788. IEEE (2016)
Redmon, J., Farhadi, A.: Yolo9000: better, faster, stronger. In: CVPR, pp. 7263–7271. IEEE (2017)
Liu, W.: 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
Khodabandeh, M., Vahdat, A., Ranjbar, M., Macready, W.G.: A robust learning approach to domain adaptive object detection. In: ICCV, pp. 480–490. IEEE (2019)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: ICCV, pp. 2223–2232. IEEE (2017)
Cordts, M., et al.: The cityscapes dataset for semantic urban scene understanding. In: CVPR, pp. 3213–3223. IEEE (2016)
Sakaridis, C., Dai, D., Van Gool, L.: Semantic foggy scene understanding with synthetic data. IJCV 126(9), 973–992 (2018)
Acknowledgments
This work was supported by the Natural Science Foundation of Tianjin (No. 21ICYBJC00640) and by the 2023 CCF-Baidu Songguo Foundation (Research on Scene Text Recognition Based on PaddlePaddle).
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Pan, G. et al. (2024). Domain Adaptive Object Detection with Dehazing Module. In: Huang, DS., Pan, Y., Zhang, Q. (eds) Advanced Intelligent Computing Technology and Applications. ICIC 2024. Lecture Notes in Computer Science, vol 14872. Springer, Singapore. https://doi.org/10.1007/978-981-97-5612-4_7
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DOI: https://doi.org/10.1007/978-981-97-5612-4_7
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