This dissertation presents a deep learning-based framework for object-level change detection in cluttered visual scenes, focusing on identifying added or reconfigured items between temporally adjacent images. The motivation arises from real-world challenges in automated waste monitoring systems, where detecting changes in bin contents is critical for optimizing collection routes, improving recycling efficiency, and reducing operational costs. To address this need, two approaches were explored. Initially, a Siamese network trained with contrastive loss was implemented to evaluate the feasibility of image-level change detection based on pairwise similarity. While this approach demonstrated potential, it was limited to producing a single similarity score between images, without the ability to localize individual changes or determine the number of added objects.