To the best of our knowledge, a systematic review for COD has not been publicly reported, especially for recently proposed deep learning-based COD models. However, most existing works primarily focus on modeling camouflaged object detection over in-depth analyzing existing COD structures. Recently, COD has attracted increasing attention in computer vision, and a few models of camouflaged object detection have been successfully explored. The experiments on three COD datasets show that our model achieves state-of-the-art performance.Ĭamouflaged object detection (COD) is an emerging visual detection task, which aims to locate and distinguish the disguised target in complex backgrounds by imitating the human visual detection system.
Leopard camouflage full#
Our method accurately detects camouflaged objects by considering full level information and a large receptive field. We also propose a HIT module to transfer the features of different dilated rates inside each level hierarchically, which expands the receptive field of each branch and enhances the relationship between different features. Our NCM not only reduces the burden of dense connection that consumes a lot of computing memory and redundant features but also weakens the phenomenon of the long-term transmission of context. NCM aggregates the features from the neighboring layers of the encoder network to enhance the complementation of various level information. In this paper, we propose a novel COD framework, which consists of two main components, namely, Neighbor Connection Mode (NCM) and Hierarchical Information Transfer (HIT). Unlike general object detection, COD is a more challenging task because the target boundaries are vague and the location is difficult to determine. The experimental results show that the improved MobileNet-SSD algorithm can detect the defects of six traditional body paint films with an accuracy rate of over 95%, which is 10% faster than the traditional SSD algorithm, and can realize real-time and accurate detection of body paint defects.Ĭamouflaged Object Detection (COD) aims to detect objects with high similarity to the background. Aiming at the defect characteristics inherent in vehicle paints, an improved MobileNet-SSD algorithm for automatic detection of paint defects is proposed by improving the feature layer of MobileNet-SSD network and optimizing the matching strategy of bounding box. The vehicle body paint defect image was collected in real time, and a new data enhancement algorithm was proposed to enhance the database for the over-fitting phenomenon caused by small sample data.
Leopard camouflage manual#
In order to improve the efficiency and accuracy of manual vehicle paint defect detection, the computer vision technology and deep learning methods is used to achieve automatic detection of vehicle paint defects based on small samples in this study. Experiments on the camouflaged people dataset show that our approach outperformed all state-of-the-art methods. At last, to locate and distinguish the camouflaged target better from the background, we develop a multi-attention module that generates more discriminative feature representation and combines semantic information with spatial information from different levels.
Furthermore, we use a dense feature pyramid taking advantage of multi-scale semantic features. We design a novel inception module to extract multi-scale receptive fields features aiming at enhancing feature representation. To overcome this problem, we present a new end-to-end framework via a multi-level attention network in this paper. Due to the nature of hidden objects, identifying them require a significant level of visual perception. That’s why seeing these hidden objects is a challenging task. Camouflaged people like soldiers on the battlefield or even camouflaged objects in the natural environments are hard to be detected because of the strong resemblances between the hidden target and the background.