摘要 : Abstract The research field of object detection has been a hotspot in computer vision. However, most of the one-stage lightweight object detection models based on the deep convolutional neural network have the problems of many par... 展开 Abstract The research field of object detection has been a hotspot in computer vision. However, most of the one-stage lightweight object detection models based on the deep convolutional neural network have the problems of many parameters. To address this problem, this paper proposes a new model named Fusion Shuffle Light Detector (FSLDet). First, based on the FSSD mode, we apply the improved lightweight Shufflenet V2 network to the FSSD model for feature extraction, where the improvement about ShuffleNet v2 is an adjustment for the network structure. Meanwhile, we adopt the bidirectional feature pyramid model to improve the feature fusion operation, which makes the fused features have more semantic information. Experiments were carried out on PASCAL VOC 2007 + 2012 dataset and helmet detection dataset. The experiment shows that the FSLDet model is superior to the state-of-the-art model in multiple evaluation criteria. 收起