Detection of Seashore Debris with Fixed Camera Images using Computer Vision and Deep learning
Anshika Kankane, Dongshik Kang · 2021
Marine debris is impacting coastal landscapes majorly by affecting biodiversity, impairing recreational uses, causing losses to fishing industries, maritime industries, etc. In this paper, a model is being proposed to detect seven types of debris categories on custom dataset using instance segmentation with shape matching network which can then be cleaned timely and efficiently. This method resulted in the improvement of misclassification of masks for objects with different illuminations, shape, occlusion and viewpoints. The manually constructed dataset for this system is created with fixed camera images, by annotating them using CV AT with seven types of labels. A pre-trained HOG shape feature extractor is being used on LIBSVM along with template matching to improve the predicted masked images obtained via Mask R-CNN training. This system intends to timely alert the cleanup organizations with the recorded debris data.