U-NET based Camaouflaged Object Detection

Because of the complex relationships between the target object and its surroundings, Camouflaged Object detection (COD) presents a unique challenge that goes beyond traditional object identification tasks. In order to detect objects that have been disguised, this work uses a 2D U-Net architecture It highlights how well this architecture preserves fine-grained spatial features due to its unique encoder-decoder structure with neglect links. Known as the identification of deliberately hidden objects that mix in with their surroundings, the detection of camouflaged objects depends on semantic segmentation, which is an area in which U-Net performs exceptionally well. The model is trained using the MCS1K training dataset, and its performance is evaluated using the MCS1K testing dataset. The average accuracy of 70.14 percent that has been reported highlights how effective the suggested method is at handling the complex problem of hidden object recognition.