This paper addresses fine-grained object detection in scenarios with limited computing resources, such as edge computing. In particular, we focus on a scenario where a single image contains objects of the same category but varying sizes, and we desire an algorithm that can not only recognize the physical class of objects but also detect their size. Deep learning (DL), particularly through the use of deep neural networks (DNNs), has become the primary approach to object detection. However, obtaining accurate fine-grained detection requires a large DNN model and a significant amount of annotated data, presenting a challenge to solve our problem particularly for resource-constrained scenarios. To this end, we propose an approach that utilizes commonsense knowledge to assist a coarse-grained object detector in achieving accurate size-related fine-grained detection results. Specifically, we introduce a commonsense knowledge inference module (CKIM) that processes the coarse-grained labels produced by a benchmark coarse-grained DL detector to generate size-related fine-grained labels. Our CKIM explores both crisp-rule and fuzzy-rule based inference methods, with the latter being employed to handle ambiguity in the target size-related labels. We implement our method based on two modern DL detectors, including Mobilenet-SSD, and YOLOv7-tiny. Experimental results demonstrate that our approach achieves accurate fine-grained detections with a reduced amount of annotated data, and smaller model size. Our code is available at https://github.com/ZJLAB-AMMI/CKIM.
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