YOLO-SM: A Lightweight Single-Class Multi-Deformation Object Detection Network

Abstract

Recently, object detection witnessed vast progress with the rapid development of Convolutional Neural Networks (CNNs). However, object detection is mainly for multi-class tasks, and few networks are used to detect single-class multi-deformation objects. This paper aims to develop a lightweight object detection network for single-class multi-deformation objects to promote the practical application of object detection networks. First, we design a Densely Connected Multi-scale (DCM) module to augment the semantic information extraction of deformation objects. With the DCM module and other strategies incorporated, we design a lightweight backbone structure for object detection, namely, DCMNet. Then, we construct a lightweight Neck structure Ghost Multi-scale Feature (GMF) module for feature fusion using a feature linear generation strategy. Finally, with the DCMNet and GMF module, we propose the object detection network YOLO-SM for single-class multi-deformation objects. Extensive experiments demonstrate that our proposed backbone structure, DCMNet, significantly outperforms the state-of-the-art models. YOLO-SM achieves 97.66% mean Average Precision ( $mAP$ ) on the Barcode public dataset, which is higher than other state-of-the-art object detection models, and achieves an inference time of 55.45 frames per second (FPS), proving that the YOLO-SM has a good performance tradeoff between speed and accuracy in detecting single-class multi-deformation objects. Furthermore, in the single-class multi-deformation Crack public dataset, the $mAP$ of 86.11% is achieved, and an $mAP$ of 99.84% is obtained in the multi-class dataset Dish20, which is much higher than other state-of-the-art object detection models, proving that the YOLO-SM has good generalization ability.