In this paper, we consider fine-grained image object detection in resource-constrained cases such as edge computing. Deep learning (DL), namely learning with deep neural networks (DNNs), has become the dominating approach to object detection. To achieve accurate fine-grained detection, one needs to employ a large enough DNN model and a vast amount of data annotations, which brings a challenge for using modern DL object detectors in resource-constrained cases. To this end, we propose an approach, which leverages commonsense knowledge to assist a coarse-grained object detector to get accurate fine-grained detection results. Specifically, we introduce a commonsense knowledge inference module (CKIM) to process coarse-grained lables given by a benchmark DL detector to produce fine-grained lables. We consider both crisp-rule and fuzzy-rule based inference in our CKIM; the latter is used to handle ambiguity in the target semantic labels. We implement our method based on several modern DL detectors, namely YOLOv4, Mobilenetv3-SSD and YOLOv7-tiny. Experiment results show that our approach outperforms benchmark detectors remarkably in terms of accuracy, model size and processing latency.

翻译：本文研究了资源受限场景（如边缘计算）下的细粒度图像目标检测问题。深度学习（DL），即基于深度神经网络的学习方法，已成为目标检测的主流技术。为实现精确的细粒度检测，通常需要采用足够大的深度神经网络模型并依赖大量标注数据，这对在资源受限情况下应用现代深度学习目标检测器提出了挑战。为此，我们提出了一种方法，利用常识知识辅助粗粒度目标检测器获得精确的细粒度检测结果。具体而言，我们引入了一个常识知识推理模块（CKIM），对基准深度学习检测器给出的粗粒度标签进行处理，以生成细粒度标签。在我们的常识知识推理模块中，同时考虑了基于清晰规则和模糊规则的推理方法，后者用于处理目标语义标签中的歧义性。我们基于多种现代深度学习检测器（包括YOLOv4、Mobilenetv3-SSD和YOLOv7-tiny）实现了该方法。实验结果表明，我们的方法在准确率、模型大小和处理延迟方面均显著优于基准检测器。