关键词: 深度学习, 目标检测, 特征融合, 样本失衡, 卷积神经网（CNN）

Abstract: In order to improve the defect of poor detection accuracy of the one-stage object detection algorithm, an efficient multi-target location detection algorithm FSD based on SSD is proposed. The algorithm mainly improves the one-stage object detection algorithm from two aspects: on the one hand, it designs a more efficient dense residual network, namely R-DenseNet, by adopting a narrower dense network structure form to maintain feature extraction. The capacity reduces the computational complexity, which improves the detection and convergence performance of the algorithm. On the other hand, the loss function is improved. By suppressing the weight of the easily-divided samples in the loss function, the robustness of the algorithm is improved, and the phenomenon of sample imbalance in object detection is improved. The Tensorflow deep learning framework is used to deploy the network, and experiments are carried out on Ubuntu equipped with Nvidia Titan X. Experiments show that FSD achieves the highest detection accuracy on both COCO and PASCAL VOC object detection data sets, among which FSD300 detection accuracy compared with the SSD300, there is a 3.7% improvement, and the detection phase rate is 10.87% higher than that of the SSD.

Key words: deep learning, object detection, feature fusion, sample imbalance, Convolutional Neural Network（CNN）