In a broad sense, camouflaged objects generally refer to objects that have a high degree of similarity to the background. Therefore, camouflaged object segmentation (COS) is more challenging than traditional object segmentation. Current COS networks have high segmentation precision on datasets. However, the problems of object miss detection and false alarm still occur, mainly due to the different camouflage levels of the objects. In this paper, we propose a Joint Comparative Learning Network (JCNet) for camouflaged object segmentation based on joint salient object for contrastive learning to overcome the widespread challenges in COS. Specifically, the main innovation of JCNet is the Contrastive Network (CNet) design, which generates a unique feature representation of the camouflaged object different from others. In terms of details, we design an edge guidance module to enhance the edge extraction capability. Moreover, a global relationship capture module is proposed to improve the confidence level of the feature representation. Finally, we set positive and negative samples and loss functions in conjunction with sample types. We conducted comprehensive experiments using four COS datasets, and the results demonstrate its suitability for COS when compared with other state-of-the-art segmentation models. JCNet achieves optimal results on five evaluation metrics, including an average improvement of 1.93% and 2.7% on F_m and F ^ω _m , respectively. In summary, it has lower miss and false alarm rates, and better generalization in the COS task. In addition, the experiments demonstrate that JCNet also has strong segmentation capability in salient object segmentation, achieving a win-win situation for both tasks. The code will be available at https://github.com/jiangxinhao2020/JCNet.

Camouflaged object detection (COD), in a broad sense, aims to detect image objects that have high degrees of similarity to the background. COD is more challenging than conventional object detection because of the high degree of “fusion” between a camouflaged object and the background. In this paper, we focused on the accurate detection of camouflaged objects, conducting an in-depth study on COD and addressing the common detection problems of high miss rates and low confidence levels. We proposed a ternary cascade perception-based method for detecting camouflaged objects and constructed a cascade perception network (CPNet). The innovation lies in the proposed ternary cascade perception module (TCPM), which focuses on extracting the relationship information between features and the spatial information of the camouflaged target and the location information of key points. In addition, a cascade aggregation pyramid (CAP) and a joint loss function have been proposed to recognize camouflaged objects accurately. We conducted comprehensive experiments on the COD10K dataset and compared our proposed approach with other seventeen-object detection models. The experimental results showed that CPNet achieves optimal results in terms of six evaluation metrics, including an average precision (AP)₅₀ that reaches 91.41, an AP₇₅ that improves to 73.04, and significantly higher detection accuracy and confidence.

In recent years, protecting important objects by simulating animal camouflage has been widely employed in many fields. Therefore, camouflaged object detection (COD) technology has emerged. COD is more difficult to achieve than traditional object detection techniques due to the high degree of fusion of objects camouflaged with the background. In this paper, we strive to more accurately and efficiently identify camouflaged objects. Inspired by the use of magnifiers to search for hidden objects in pictures, we propose a COD network that simulates the observation effect of a magnifier called the MAGnifier Network (MAGNet). Specifically, our MAGNet contains two parallel modules: the ergodic magnification module (EMM) and the attention focus module (AFM). The EMM is designed to mimic the process of a magnifier enlarging an image, and AFM is used to simulate the observation process in which human attention is highly focused on a particular region. The two sets of output camouflaged object maps were merged to simulate the observation of an object by a magnifier. In addition, a weighted key point area perception loss function, which is more applicable to COD, was designed based on two modules to give greater attention to the camouflaged object. Extensive experiments demonstrate that compared with 19 cutting-edge detection models, MAGNet can achieve the best comprehensive effect on eight evaluation metrics in the public COD dataset. Additionally, compared to other COD methods, MAGNet has lower computational complexity and faster segmentation. We also validated the model’s generalization ability on a military camouflaged object dataset constructed in-house. Finally, we experimentally explored some extended applications of COD.

Adversarial attacks that mislead deep neural networks (DNNs) into making incorrect predictions can also be implemented in the physical world. However, most of the existing adversarial camouflage textures that attack object detection models only consider the effectiveness of the attack, ignoring the stealthiness of adversarial attacks, resulting in the generated adversarial camouflage textures appearing abrupt to human observers. To address this issue, we propose a style transfer module added to an adversarial texture generation framework. By calculating the style loss between the texture and the specified style image, the adversarial texture generated by the model is guided to have good stealthiness and is not easily detected by DNNs and human observers in specific scenes. Experiments have shown that in both the digital and physical worlds, the vehicle full coverage adversarial camouflage texture we create has good stealthiness and can effectively fool advanced DNN object detectors while evading human observers in specific scenes.

Improving the clinical ability and quality of endocrinology department with diagnosis-related groups tool Sheng Ding, Xiaowan Jiang, Yuming Zheng, Tao Feng, Hong Mao, Zhi Chen, Wei Cai, Zhongjing Wang