A Proposal for Automatic Bounding Box Annotation for Object Detection Tasks

Object detection is a crucial task in computer vision with applications in a plethora of fields such as autonomous driving, medical imaging, surveillance, and robotics. The effectiveness of object detection models heavily depends on the availability of large-scale annotated datasets, where objects are labeled with precise bounding boxes. However, the manual annotation of images to generate bounding boxes is a time-consuming and labor-intensive process, often requiring domain-specific expertise. In this paper, we propose a method to automatically generate bounding boxes from Class Activation Maps images, with the aim of generate object detection datasets annotated with bounding boxes derived from these heatmaps. The proposed approach leverages the localized information provided by Class Activation Maps to detect and isolate regions of interest, encapsulating them within bounding boxes. The proposed method has broad applicability across various domains, including medical imaging, cybersecurity, and industrial inspection, where annotated datasets are often scarce.