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Object detection in kitchen waste faces numerous challenges, including a variety of target categories, significant morphological variations, and complex backgrounds, coupled with the scarcity of targeted datasets, resulting in limited research in this area. In response to these challenges, we integrate insights from real-world industrial scenarios of kitchen waste sorting and create, for the first time, a dataset comprising eight categories specifically designed for kitchen waste object detection. Furthermore, we initiate the integration of active learning (AL) methods into the object detection transformer (DETR) network, proposing the AL-DETR model—a high-precision detection model tailored for scenarios with limited samples. To overcome the drawback of DETR, which relies on a large amount of annotated data for training, we employ an AL strategy, annotating challenging samples with high uncertainty selected from a pool of unlabeled data, and integrating them back into the training set to enhance the model’s learning capability. AL-DETR also models non-local visual correlations between labeled and unlabeled samples, comparing differences in detection categories to identify relevant unlabeled samples. Additionally, we establish a robotic sorting platform with an end-effector integrating gripping and suction functionalities, accompanied by the development of visual detection and robot control software. The experimental results indicate that, in the dataset constructed for this study, which primarily focuses on detecting large objects, our approach achieves a minimum improvement of 9.23% compared to other methods, reaching state-of-the-art (SOTA) performance. Additionally, our method surpasses other AL approaches using the entire annotated dataset by more than 1.01% in mAP when the original dataset’s annotation data is only 82.5%. In practical testing, the sorting platform successfully accomplishes efficient detection and sorting of kitchen waste.

In skin lesion detection systems, a large number of labeled images are usually required to achieve high segmentation accuracy (ACC), which hinders the effectiveness and timeliness of disease diagnosis. To this end, a high-precision skin lesion detection system is proposed in this article. The hardware part of the system adopts a modular design, fully considering ergonomics, portability, and miniaturization. In the software part, an active learning ensemble with a multimodel fusion method (ALEM) is proposed to achieve efficient and accurate skin lesion region segmentation. The core idea of ALEM is to use multiple uncertainty strategies of active learning to obtain the most uncertain pixels to be marked in the skin lesion image when marking image pixels. The experiment shows that the average Dice coefficient (DIC) and average Jaccard index (JAI) of ALEM on International Skin Imaging Collaboration (ISIC)-2016 are 82.81% and 92.4%, respectively, and that on ISIC-2017 are 87.51% and 79.26%, respectively. It is worth noting that ALEM still outperforms in tests with only 80% of the training data and no more than 15% pixel annotation per image on average. Our system achieves an average AUC of 91.01% on the ISIC2017 and is tested for effectiveness on real skin. The skin lesion detection system developed in this article is expected to bring convenience to doctors and patients and speed up the diagnosis of diseases.