Camouflaged people detection based on a semi-supervised search identification network

Yang, Liu; Wang, Congqing; Zhou, Yongjun

doi:10.1016/j.dt.2021.09.004

Cited by 15 publications

(9 citation statements)

References 13 publications

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“…Finally, they conducted experiments on the COD10K dataset. The experiment findings illustrated that their algorithm had higher accuracy in detecting disguised individuals [10]. The Tran team simulated parameters such as geometric distance of castings based on flat panel detectors for monitoring small and medium-sized castings in cone beam computers.…”

Section: Related Workmentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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With the upgrading of aviation space technology, the amount of information contained in remote sensing images in the aviation is gradually increasing, and the detection technology based on small targets has developed. For lightweight small targets, pixels per unit area contain more information than large targets, and their area is too small, which is easily overlooked by conventional detection models. To enhance the attention of such algorithms, this study first introduces a Control Bus Attention Mechanism (CBAM) in the fifth generation You Only Look Once (YOLOv5) algorithm to increase the algorithm’s attention to small targets and generate optimization algorithms. Then convolutional neural network is used to mark feature pixels of the optimization algorithm, eliminate redundant information, and generate fusion algorithm, which is used to generate redundant information with high similarity when the optimization algorithm surveys pixel blocks. The novelty of this study lies in using CBAM to improve YOLOv5 algorithm. CBAM module can extract important features from images by adaptively learning the channel and spatial attention of feature maps. By weighting the channel and spatial attention of the feature map, the network can pay more attention to important features and suppress irrelevant background information. This attention mechanism can help the network better capture the characteristics of small targets and improve the accuracy and robustness of detection. Embedding CBAM module into YOLOv5 detection network can enhance the network's perception of small targets. CBAM module can improve the expressive ability and feature extraction ability of the network without increasing the complexity of the network. By introducing CBAM module, YOLOv5 can better capture the characteristics of small targets in aerial remote sensing images, and improve the detection accuracy and recall rate. Finally, the proposed fusion algorithm is used for experiments on the Tiny-Person dataset and compared with the fifth, sixth, and seventh generations of You Only Look Once. When the fusion algorithm tests the target, the classification accuracy of Sea-person is 39 %, the classification accuracy of Earth-person is 31 %, and the probability of being predicted as the background is 56 % and 67 %, respectively. And the overall accuracy of this algorithm is 0.987, which is the best among the four algorithms. The experimental results show that the fusion algorithm proposed in the study has precise positioning for lightweight small targets and can achieve good application results in aerial remote sensing images.

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Section: Related Workmentioning

confidence: 99%

Lightweight small target detection based on aerial remote sensing images

2024

J. meas. eng.

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“…Liu et al [15] have proposed semi-supervised search identification network (Semi-SINet) based camouflaged military people detection system, in which, the camouflaged object detection dataset (COD10K) was taken into the consideration. It was observed that the proposed approach performance better than the existing approaches.…”

Section: Literature Reviewmentioning

confidence: 99%

Revealing Hidden Patterns: A Deep Learning Approach to Camouflage Detection

Kamble,

Rajarajeswari

2024

IJCMEM

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In military defence and wildlife conservation operations, detecting camouflage in images poses a significant challenge. This research investigates the efficacy of deep learning techniques, including Convolutional Neural Networks (CNN), Artificial Neural Networks (ANN), and Long Short-Term Memory (LSTM), in addressing this challenge. The study meticulously evaluates each model's performance using metrics such as average accuracy, validation accuracy, and loss measures across well-known benchmark datasets comprising camouflaged and non-camouflaged images. Notably, the CNN + ANN Pipeline model emerges as the most effective, achieving a remarkable average accuracy of 91.37%. This model, together with the standalone CNN, outperforms the ANN and LSTM models in terms of camouflage detection. The discoveries advance the state-of-the-art in image analysis while also having practical implications for real-world applications. In military settings, good camouflage detection can improve situational awareness and danger detection capabilities. Similarly, automated camouflage detection helps monitor and protect endangered species by detecting hidden creatures or potential threats. Overall, this study highlights the ability of deep learning techniques to greatly improve visual analytic tasks across a variety of domains.

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“…Generating a smooth sequence of bounding boxes is presented in this paper to track camouflaged targets in unlabelled video. The camouflaged object segmentation method we previously investigated [20] was used to discover moving targets from complex backgrounds and generate initial detection boxes Bt by establishing the minimum bounding rectangle of the camouflaged target. During UAV reconnaissance, the generated candidate bounding boxes may appear as noisy boxes due to UAV jitter or target occlusion.…”

Section: Figure 2 Schematic Of Candidate Frame Generation Based On Ca...mentioning

confidence: 99%

An unsupervised aerial tracking method of camouflaged targets in complex environments

Yang

Wang

et al. 2023

J. Phys.: Conf. Ser.

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With the widespread use of UAVs and the development of various technologies related to computer vision, much attention has been devoted to UAV-based target tracking. For the task of tracking camouflaged people in complex environments, there is a shortage of annotated datasets and poor backbone feature extraction network capabilities, which will cause tracking drift and tracking lag. To achieve unsupervised UAV target tracking under unsupervised conditions, a new target tracking model has been designed. First, we roughly detect moving targets using a camouflaged segmentation network, and then apply dynamic programming to generate smooth candidate boxes. Secondly, a deeper convolutional neural network is designed for feature extraction to replace the original backbone network, and the residual structure is modified to ensure tracking effectiveness. Extensive comparative experiments have shown that the proposed unsupervised method performs the task of UAV target tracking well.

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Camouflaged people detection based on a semi-supervised search identification network

Cited by 15 publications

References 13 publications

Lightweight small target detection based on aerial remote sensing images

Lightweight small target detection based on aerial remote sensing images

Revealing Hidden Patterns: A Deep Learning Approach to Camouflage Detection

An unsupervised aerial tracking method of camouflaged targets in complex environments

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