Tran Tuan Minh * , Tran Van Bao , Vo Duy Nguyen and Nguyen Tan Tran Minh Khang

* Corresponding author (

Main Article Content


Image processing and object detection in aerial images have to deal with a lot of trouble due to the existence of haze, smoke, dust in the atmosphere. These factors can blur objects and severely decline image quality which might lead to incorrect or missing object detection. To solve this problem, this study shows a method that can reduce the bad effect of haze on object detection in aerial images. A combination of a dehazing method called Feature Fusion Attention Network (FFA-Net) and an object detection method named Probabilistic Anchor Assignment (PAA) was conducted to evaluate two hypotheses: (1) haze was a noisy factor and (2) haze was treated as part of objects. Through extensive experiments, the selective dehazing hypothesis, which was used for truck objects, improved the detection result of car and bus from 19.6% to 21.9% and 0.7% to 4.4%, respectively, on the UAVDT-Benchmark-M dataset. This result showed that our approach was effective.

Keywords: Object detection, dehazing, aerial images, deep learning

Article Details


Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). Yolov4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934.

Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016). Dehazenet: An end-to-end system for single image haze removal. IEEE Transactions on Image Processing, 25(11), 5187-5198.

Chen, D., He, M., Fan, Q., Liao, J., Zhang, L., Hou, D., ... & Hua, G. (2019a). Gated context aggregation network for image dehazing and deraining. In 2019 IEEE winter conference on applications of computer vision (WACV) (pp. 1375-1383). IEEE.

Chen, K., Wang, J., Pang, J., Cao, Y., Xiong, Y., Li, X., ... & Lin, D. (2019b). MMDetection: Open mmlab detection toolbox and benchmark. arXiv preprint arXiv:1906.07155.

Chung, Q. M., Le, T. D., Dang, T. V., Vo, N. D., Nguyen, T. V., & Nguyen, K. (2020). Data augmentation analysis in vehicle detection from aerial videos. 2020 RIVF International Conference on Computing and Communication Technologies (RIVF) (pp. 1-3). IEEE.

Du, D., Qi, Y., Yu, H., Yang, Y., Duan, K., Li, G., ... & Tian, Q. (2018). The unmanned aerial vehicle benchmark: Object detection and tracking. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 370-386).

He, K., Sun, J., & Tang, X. (2010). Single image haze removal using dark channel prior. IEEE transactions on pattern analysis and machine intelligence, 33(12), 2341-2353.

He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778).

Kim, K., & Lee, H. S. (2020). Probabilistic anchor assignment with iou prediction for object detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXV 16 (pp. 355-371). Springer International Publishing.

Li, B., Peng, X., Wang, Z., Xu, J., & Feng, D. (2017). Aod-net: All-in-one dehazing network. In Proceedings of the IEEE international conference on computer vision (pp. 4770-4778).

Li, B., Ren, W., Fu, D., Tao, D., Feng, D., Zeng, W., & Wang, Z. (2018). Benchmarking single-image dehazing and beyond. IEEE Transactions on Image Processing, 28(1), 492-505.

McCartney, E. J. (1976). Optics of the atmosphere: scattering by molecules and particles (1st ed.). Wiley, New York.

Narasimhan, S. G., & Nayar, S. K. (2000, June). Chromatic framework for vision in bad weather. In Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No. PR00662) (Vol. 1, pp. 598-605). IEEE.

Narasimhan, S. G., & Nayar, S. K. (2002). Vision and the atmosphere. International Journal of Computer Vision, 18(3) 233-254.

Nguyen, K., Huynh, N. T., Nguyen, P. C., Nguyen, K. D., Vo, N. D., & Nguyen, T. V. (2020). Detecting objects from space: An evaluation of deep-learning modern approaches. Electronics, 9(4), 583.

Qin, X., Wang, Z., Bai, Y., Xie, X., & Jia, H. (2020, April). FFA-Net: Feature fusion attention network for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence, 34(7), 11908-11915.

Ren, W., Liu, S., Zhang, H., Pan, J., Cao, X., & Yang, M. H. (2016, October). Single image dehazing via multi-scale convolutional neural networks. In European conference on computer vision (pp. 154-169). Springer, Cham.

Vu, T., Kang, H., & Yoo, C. D. (2021, May). SCNet: Training Inference Sample Consistency for Instance Segmentation. In Proceedings of the AAAI Conference on Artificial Intelligence, 35(3), 2701-2709.

Yang, D., & Sun, J. (2018). Proximal dehaze-net: A prior learning-based deep network for single image dehazing. In Proceedings of the European Conference on Computer Vision (ECCV) (pp. 702-717).

Yang, Z., Liu, S., Hu, H., Wang, L., & Lin, S. (2019). Reppoints: Point set representation for object detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 9657-9666).

Zhang, H., & Patel, V. M. (2018). Densely connected pyramid dehazing network. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 3194-3203).