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）

摘要： 为改善一阶段目标检测算法检测精度较差的缺陷，提出一种基于SSD的高效多目标定位检测算法FSD。该算法主要从两个方面对一阶段目标检测算法进行改进：设计了一个更高效的密集残差网络，即R-DenseNet，通过采用一种更窄的密集网络结构形式，在保持特征提取容量的同时降低了计算复杂度，从而提高了算法的检测和收敛性能；改进了损失函数，通过抑制易分样本在损失函数中的权重，提高算法的鲁棒性，改善了目标检测中样本失衡的现象。采用Tensorflow深度学习框架部署网络，并在搭载Nvidia Titan X的Ubuntu上开展实验，实验表明FSD在COCO和PASCAL VOC这两个目标检测数据集上上都取得了最高的检测精度，其中FSD300D的检测精度相比SSD300有3.7%提升，检测相率比SSD有10.87%提升。

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）