基于SENet改进的Faster R-CNN行人检测模型

随着无人驾驶和智能驾驶技术的发展，计算机视觉对视频图像检测的实时性和准确性要求也越来越高.现有的行人检测方法在检测速度和检测精度两个方面难以权衡.针对此问题，提出一种改进的Faster R-CNN模型，在Faster R-CNN的主体特征提取网络模块中加入SE网络单元，进行道路行人检测.这种方法不仅能达到相对较高的准确率，用于视频检测时还能达到一个较好的检测速率，其综合表现比Faster R-CNN模型更好.在INRIA数据集和私有数据集上的实验表明，模型的mAP最好成绩能达到93.76%，最高检测速度达到了13.79 f/s.

Pedestrian Detection Model Based on Improved Faster R-CNN with SENet

Computer vision is an important branch of machine learning at present, which requests much higher instantaneity and accuracy as the driverless and SI-Drive development. To optimize the current methods, the Faster Region-based Convolutional Neural Network (Faster R-CNN) is upgraded by adding SENet to it in this study. The upgraded Faster R-CNN model is applied in pedestrian detection. The new model does not only bring higher accuracy but also accomplish a better detection rate. To verify the new method, an examine was done in INRIA set and our set. The result shows that the upgraded model has a better detection performance on both accuracy and rate which can meet the related specifications of real-time pedestrian detection basically. Finally, the method was tested in the NVIDIA GTX1080Ti GPU. The results show that the mAP of upgraded model can achieve up to 92.7%, while the detection rate is up to 13.79 f/s under a relatively plain experimental condition. On the whole, the new model performs better than the traditional Faster R-CNN model.