基于显著图融合的无人机载热红外图像目标检测方法

Object Detection Method Based on Saliency Map Fusion for UAV-borne Thermal Images

利用无人机载的热红外图像开展行人及车辆检测,在交通监控、智能安防、防灾应急等领域中,具有巨大的应用潜力.热红外图像能够在夜间或者光照条件不理想的情况对场景目标清晰成像,但也往往存在对比度低、纹理特征弱的缺点.为此,本文提出使用热红外图像的显著图来进行图像增强,作为目标检测器的注意力机制,并研究仅使用热红外图像和其显著图提高目标检测性能的方法.此外,针对无人机内存不足、算力有限的特点,设计使用轻量化网络YOLOv3-MobileNetv2作为目标检测模型.在实验中,本文训练了YOLOv3网络作为检测的评价基准网络.使用BASNet生成显著图,通过通道替换和像素级加权融合两种方案将热红外图像与其对应的显著图进行融合增强,比较了不同方案下YOLOv3-MobileNetv2模型的检测性能.统计结果显示,行人及车辆的平均精确度(Average precision,AP)相对于基准分别提升了6.7%和5.7%,同时检测速度提升了60%,模型大小降低了58%.该算法模型为开拓无人机载热红外图像的应用领域提供了可靠的技术支撑.

Using thermal images obtained from unmanned aerial vehicles(UAV)for pedestrian and vehicle detection has great potential in the fields of traffic monitoring,intelligent security,disaster prevention,and emergency response.Thermal images can clearly observe objects at night or under bad lighting conditions,but they also have the disadvantages of low contrast and weak texture features.For these reasons,this paper proposes to use the saliency map of the thermal image for image enhancement as the attention mechanism of the object detector.The technology to improve the performance of object detection using only thermal images and their saliency maps is studied.In addition,considering the computing power of UAV platforms,a lightweight network YOLOv3-MobileNetv2 was designed as the object detection model.In the paper,YOLOv3 network is trained as a detection benchmark;BASNet is used to generate saliency maps.We fuse thermal images with their corresponding saliency maps through channel replacement and pixel-level weighted fusion schemes.In our experiments,the detection performances of YOLOv3-MobileNetv2 model with different schemes are compared.The statistical results show that the average precision(AP)of pedestrians and vehicles are increased by 6.7%and 5.7%respectively,compared with the benchmark.The detection speed is increased by 60%,while the model size is reduced by 58%.This model provides reliable technical support for the application of thermal images with UAV platforms.