Long-tailed object detection of kitchen waste with class-instance balanced detector

Intelligent detection and classification of kitchen waste can promote ecological sustainability by replacing inefficient manual processes. However, the presence of non-degradable waste mixed in kitchen waste often follows a long-tailed distribution, making it challenging to train convolutional neural network-based object detectors, which results in the unsatisfactory detection of tail-class waste. To address this challenge, we propose a class-instance balanced detector (CIB-Det) for intelligent detection and classification of kitchen waste. CIB-Det implements two strategies for the loss function: the class-balanced strategy (CBS) and the instance-balanced strategy (IBS). The CBS focuses more on tail classes, and the IBS concentrates on hard-to-classify instances adaptively during training. Consequently, CIB-Det comprehensively and adaptively addresses the long-tailed issue. Our experiments on a real dataset of kitchen waste images support the effectiveness of CIB-Det for kitchen waste detection.