Household Garbage

Household garbage management is a critical area of research focusing on efficient waste sorting, monitoring, and disposal to mitigate environmental pollution and resource depletion. Current research employs computer vision techniques, particularly convolutional neural networks and transformer-based models like Grounding DINO and CLIP, to automate garbage classification and detection in various settings, from household bins to marine environments. These advancements leverage image recognition and segmentation algorithms, often utilizing newly created datasets for training and benchmarking, to improve the accuracy and efficiency of waste management processes. The ultimate goal is to develop robust and scalable solutions for sustainable waste management practices, impacting both environmental protection and public health.