Three-Channel Infrared Imaging for Object Detection in Haze

Object detection is of wide application for its capability in recognizing and locating targets in scenes. Under heavy hazy conditions, however, the detection performance on RGB images will greatly degrade since the image contents are polluted. In this work, we propose an imaging system to acquire three-channel images in the shortwave infrared (SWIR) spectrum to facilitate object detection under hazy conditions. The system captures SWIR images in the form of pseudo color that are most suitable for detecting objects, such as pedestrians and vehicles. Two different types of filters, i.e., liquid crystal tunable filter (LCTF) and optical filters, are employed in our imaging system design. We use the LCTF to acquire narrowband hyperspectral images, which are fed into a band simulation model to generate wideband images for optimal band selection. We present a specific measure called recognition and localization (RL) score to evaluate the detection performance of three-band combinations. Based on the measure, optimal bands are determined using an efficient searching algorithm. Then, we customize three optical filters and install them on a filter wheel, with which we can acquire three-channel images in the SWIR spectrum. The effectiveness of our imaging system is evaluated on a self-collected RGB-SWIR image dataset. Experimental results indicate that, compared with the RGB images after haze removal, the three-channel SWIR images acquired by our imaging system are of higher quality and can achieve better object detection performance under hazy conditions.