Camouflage Object Detection based on Mixed Multi-scale and Multi-attention Network
Authors: 
Chunhui Zhao, Yixuan Zhang, Nan Su, and Yiming YanAuthors Info & Claims
Chunhui Zhao, Yixuan Zhang, Nan Su, and Yiming YanAuthors Info & ClaimsJanuary 2024
Pages 32 - 38
Abstract
Because the camouflage object and the background are very similar in texture and color, camouflage object detection is a challenging task. In order to improve the accuracy of camouflage object detection, a Mixed Multi-Scale and Multi-Attention Network (MMN-Net) is proposed. Firstly, the multi-scale information representation ability is enriched by the triplet backbone and hybrid multi-scale attention module. Secondly, the conditional attention mechanism enables the network to better learn the input global information, pay attention to the feature information of important regions, and improve the network feature expression ability and extraction ability. The detection accuracy of the proposed method in the camouflage object dataset is 75.4 %, which is 3.9 % higher than the baseline model. The network also has excellent performance in the detection of objects at different scales. And the accuracy is higher than other comparison algorithms. The results show that the network can effectively improve the detection rate of camouflage objects.
References
[1]
Deng Ping Fan, Ge Peng Ji, Guolei Sun, Ming Ming Cheng, and Ling Shao. Camouflaged object detection. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020: 2774-2784.
[2]
Mei, Haiyang and Ji, Ge-Peng and Wei, Ziqi and Yang, Xin and Wei, Xiaopeng and Fan, Deng-Ping. Camouflaged Object Segmentation With Distraction Mining. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2021: 2104.10475.
[3]
Yan, Jinnan and Le, Trung-Nghia and Nguyen, Khanh-Duy and Tran, Minh-Triet and Do, Thanh-Toan and Nguyen, Tam V. 2021. MirrorNet: Bio-Inspired Camouflaged Object Segmentation. IEEE Access, 2021,9: 43290-43300.
[4]
Chen T, Xiao J, Hu X,et al. 2022. Boundary-guided network for camouflaged object detection. Knowledge-based systems.
[5]
Jie Hu, Li Shen, and Gang Sun. Squeeze-and-excitation networks. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2019: 1709.01507.
[6]
Q. Wang, B. Wu, P. Zhu, P. Li, W. Zuo and Q. Hu, 2020. ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2020: 1910.03151.
[7]
Qibin Hou and Daquan Zhou and Jiashi Feng. Coordinate Attention for Efficient Mobile Network Design. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR), 2021: 13713-13722.
[8]
A. Haque, A. Alahi, and L. Fei-Fei. Recurrent attention models for depth-based person identification. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 2016: 1611.07212.
[9]
Yang B, Bender G, Le Q V, 2019. Condconv: Conditionally parameterized convolutions for efficient inference. Advances in Neural Information Processing Systems, 2019, 32.
[10]
Sanghyun Woo, Jongchan Park, Joon-Young Lee, and In So Kweon. 2018. Cbam: Convolutional block attention module. In Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany, 2018: 3-19.
[11]
Liu, Shu and Qi, Lu and Qin, Haifang and Shi, Jianping and Jia, Jiaya. 2018. Path aggregation network for instance segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition.
[12]
Loshchilov, Ilya and Hutter, Frank. 2016. Sgdr: Stochastic gradient descent with warm restarts.
[13]
Zheng Yunfei, X. Zhang, W. Feng, T. Cao, M. Sun, and W. Xiaobing. 2018. Detection of people with camouflage pattern via dense deconvolution network. Advances in IEEE Signal Processing Letters, 2018, 26(1): 29-33.
[14]
S. Ren, K. He, R. Girshick, and J. Sun. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Advances in neural information processing systems, 2015, 28.
[15]
Jiangmiao Pang, Kai Chen, Jianping Shi, Huajun Feng, Wanli Ouyang, and Dahua Lin. 2019. Libra r-cnn: Towards balanced learning for object detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, CA, June 2019.
[16]
Zhaowei Cai, and Nuno Vasconcelos. 2018. Cascade r-cnn: Delving into high quality object detection. In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, UT, 2018: 6154-6162.
[17]
Kaiming He, Georgia Gkioxari, Piotr Dollar, and Ross Girshick. Mask r-cnn. 2017. In Proceedings of the IEEE international conference on computer vision, Long Beach, CA, 2017: 2961–2969.
[18]
Hongkai Zhang, Hong Chang, Bingpeng Ma, Naiyan Wang, and Xilin Chen. 2020. Dynamic r-cnn: Towards high quality object detection via dynamic training. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020: 260–275.
[19]
Xin Lu, Buyu Li, Yuxin Yue, Quanquan Li, and Junjie Yan. 2019. Grid r-cnn. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019: 7363-7372.
[20]
Z. Tian, C. Shen, H. Chen, and T. He. 2019. Fcos: Fully convolutional one-stage object detection. In Proceedings of the IEEE/CVF international conference on computer vision, Long Beach, CA, 2019: 9627-9636.
[21]
Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander CBerg. 2016. Ssd: Single shot multibox detector. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, 2016: 21–37.
[22]
Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar. 2017. Focal loss for dense object detection. In Proceedings of the IEEE international conference on computer vision, Long Beach, CA, 2017: 2980–2988.
[23]
Chien-Yao Wang, Alexey Bochkovskiy, and Hong-Yuan Mark Liao. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). 2023: 7464-7475.
[24]
Zheng Ge, Songtao Liu, Feng Wang, Zeming Li, and Jian Sun. 2021. Yolox: Exceeding yolo series in 2021[OL]. (2021-08-06) [2022- 10- 04].https://doi.org/10.48550/arXiv.2107.08430.
Index Terms
- Camouflage Object Detection based on Mixed Multi-scale and Multi-attention Network
Recommendations
Pyramid attention object detection network with multi-scale feature fusion
Highlights- A multi-scale feature fusion pyramid attention module is proposed to better capture global and local features and improve the performance of object ...
AbstractWith the development of deep learning, object detection has made substantial progress. However, when the object to be detected in the image is small or partially occluded, the detection network often fails to detect it successfully. We ...
Graphical abstractDisplay Omitted
AGFNet: Attention Guided Fusion Network for Camouflaged Object Detection
Artificial IntelligenceAbstractThe camouflaged object detection (COD) task is challenging because of the high similarity between target and background. Most of the existing COD methods are based on the transfer learning of salient object detection (SOD) network, which is not ...
Multi-scale Feature Fusion UAV Image Object Detection Method Based on Dilated Convolution and Attention Mechanism
ICIT '20: Proceedings of the 2020 8th International Conference on Information Technology: IoT and Smart CityDue to the influence of the shooting angle of view and the flight height, the images taken by UAV often have complex backgrounds and contain a large number of small and unevenly distributed objects. In order to solve the problem that it is difficult to ...
Comments
Information & Contributors
Information
Published In
January 2024
480 pages
ISBN:9798400716720
DOI:10.1145/3647649
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 03 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
- Research
- Refereed limited
Conference
ICIGP 2024
ICIGP 2024: 2024 the 7th International Conference on Image and Graphics Processing
January 19 - 21, 2024
Beijing, China
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 9Total Downloads
- Downloads (Last 12 months)9
- Downloads (Last 6 weeks)5
Other Metrics
Citations
View Options
Get Access
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign inFull Access
View options
View or Download as a PDF file.
PDFeReader
View online with eReader.
eReaderHTML Format
View this article in HTML Format.
HTML Format