A Lightweight Deep Learning Network for Mouth Cavity Object Visual Detection

Mouth cavity object detection plays an important role in nucleic acid sampling and disease diagnosis of mouth cavity. However, due to the different sizes, similar colors, and blurred edges of mouth cavity objects, it is difficult for current object detection methods to achieve good detection results. The trade-off between the accuracy and speed of mouth cavity objects is still a challenging problem. Therefore, this paper proposes a lightweight deep learning network for mouth cavity object visual detection, which can achieve high accuracy and fast mouth cavity object detection. The network is designed based on the single shot multibox detector (SSD). First, a spatial-channel attention module with feature fusion is proposed to fuse shallow and deep feature maps. Second, a biological structure constraint loss is proposed to facilitate the network to learn the spatial relationship features of mouth cavity objects. Experimental results on a custom mouth cavity object detection dataset show that the proposed lightweight mouth cavity object detector can reach 81.3% of mAP with Intersection over Union (IoU) = 0.5:0.95 at 104 fps, which outperforms many state-of-the-art methods. Furthermore, the proposed mouth cavity object detector is also applied to an oropharyngeal swab robot (OP-Swab robot) for oropharynx sampling region detection and localization, which can achieve 96.67% sampling success accuracy.