Commonsense Knowledge Assisted Deep Learning for Resource-constrained and Fine-grained Object Detection

This paper addresses fine-grained object detection in scenarios with limited computing resources, such as edge computing. 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 for modern DL object detectors in resource-constrained cases. To address this issue, we propose an approach that utilizes commonsense knowledge to assist a coarse-grained object detector in achieving accurate 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 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 semantic 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.