深度学习与图像融合的行人检测算法研究

为解决智能辅助驾驶技术中可见光摄像机受光照和气候影响而导致行人目标识别困难的问题。通过研究图像融合技术，结合深度卷积神经网络，实现并改进了一种道路行人目标检测算法。方法是利用多源传感器图像融合技术，采用可见光相机与红外热成像相机融合的策略，以Faster RCNN算法为基础，从改进网络结构、特征融合、优化模型训练等方面展开研究，对复杂环境下的行人检测与定位跟踪展开研究，提出一种基于图像融合技术和改进的深度卷积神经网络的道路行人目标检测算法。实验结果表明，该算法对复杂气候环境下行人目标检测提高了检测效率和准确率，增加了智能辅助驾驶汽车的安全性。

Research on pedestrian detection algorithm combining deep learning and imaging fusion

The aim of this paper is to address the difficulty in pedestrian target recognition in intelligent assisted driving systems due to the influence of light and climate on visible light cameras.A pedestrian target detection algorithm is implemented and improved by studying image fusion techniques in combination with deep convolutional neural networks.Firstly,using multi source sensor image fusion technology,the strategy of fusing visible light cameras and infrared thermal imaging cameras,based on the Faster RCNN algorithm,a pedestrian target detection algorithm based on infrared thermal imaging technology and improved depth convolutional neural network is proposed.Then,the research is carried out in terms of improving network structure,feature fusion,optimising model training,and so on,and the research is carried out on pedestrian detection and localisation tracking in complex environments.Finally,the experimental results show that this algorithm improves detection efficiency and accuracy for human target detection in complex climate environments,and increases the safety of intelligent assisted driving vehicles.