LightCSPNet: A Lightweight Network for Image Classification and Objection Detection

Abstract

In recent years, computer vision and convolutional neural networks have been gradually applied in embedded devices. However, due to the limitation of hardware, the inference speed of many high-precision algorithms is very slow, which requires high performance hardware. In this study, a lightweight network called LightCSPNet is proposed for image classification and object detection. LightCSPNet is built by stacking four identical modules, each of which has adopted an improved CSP (Cross-Stage-Partial-connections) structure for channel number expansion. The special inverse residual structure is constructed for feature extraction, and the transformer modules are added in the proposed model. In this study, the typical defect detection in industry is adopted as testing platform, and a defect dataset consisting of 12 categories including cloth, road, bridge, steel and etc., was constructed for image classification. Compared with MobileNetV3, our model has almost the same accuracy, but the number of parameters and GFLOPs (Giga Floating-point Operations Per Second) have been, respectively, reduced to 88% and 36% for ImageNet100 and the dataset we built. In addition, compared with MobileNetV2 and MobileNetV3 for VOC2012 dataset in object detection, LightCSPNet obtained 0.4% and 0.6% mAP (Mean Average Precision) improvement respectively, and the inference speed on CPU was twice as fast.