Advanced EMSAGNN: An Effective Deep Learning Approach for Comprehensive Camouflage Detection and Classification

Novel network designs for deep learning show promise in overcoming the inherent similarity between hidden objects and the background environment, which hinders automatic detection and segmentation of camouflaged objects and improves detection accuracy. Nevertheless, the current network topologies for differentiating between camouflaged objects and their backgrounds need to consider the limitation of detection speed, leading to increased computational complexity and an inability to fulfil the demands of quick detection. This research proposes a new deep learning-based approach, Enhanced Multi-Head Self-Attention Graph Neural Networks (EMSAGNN), to detect and classify camouflage objects. In the pre-processing stage, noise reduction will be performed using a median filter, colour normalization, and contrast enhancement using the Adaptively Clipped Contrast Limited Adaptive Histogram Equalization (ACCLAHE) method. Then, extract the relevant features such as colour, shape, and texture from a pre-processed image using the ResNeXt50 technique. After that, detect the camouflaged object from the pre-processed input image with extracted features using the YOLOv8 detection method. Then, the Improved African Vulture Segmentation Algorithm (IAVSA) will be utilized to segment the identified camouflage objects. Finally, the novel deep learning technique is employed to classify the camouflage objects into their respective classes. According to extensive tests conducted on four challenging benchmark datasets: CHAMELEON, CAMO, COD10K, and NC4K, the proposed approach performs better than many cutting-edge techniques in terms of accuracy and model parameters.