Lightweight Thermal Super-Resolution and Object Detection for Robust Perception in Adverse Weather Conditions

Abstract

In this work, we examine the potential application of thermal cameras in improving perception capabilities in adverse weather conditions like snow, night-time driving, and haze, focusing on retaining the performance of Advanced Driver Assistance Systems (ADAS), thus enhancing its functionality and safety characteristics. While thermal sensors offer the advantage of robust information capture in adverse weather conditions, their integration is plagued with issues surrounding poor feature capture in normal conditions, low imaging resolution, and high sensor costs. We address the former by formulating the problem definition as information switching wherein thermal images are selected when visible images are degraded. Furthermore, we consider a single object detector for RGB and thermal images to ensure low latency. We propose utilizing a learnable projection function that translates the thermal image into RGB color space, thus providing minimal modifications to the underlying object detector. We address the issues of low imaging resolution and cost by proposing a novel procedure that combines super-resolution and object detection, enabling the utilization of low-resolution and low-cost uncooled thermal imaging sensors. To ensure the complete pipeline meets the actual deployment requirements of real-time inference on resource-constrained devices, we introduce a lightweight super-resolution algorithm, implementing optimizations within the network structure followed by global pruning. In addition, to improve the feature representations extracted by lightweight encoders, we propose a bidirectional feature pyramid network to enhance the feature representation. We demonstrate the efficacy of the proposed mechanism through extensive simulated evaluations on automotive datasets such as FLIR, KAIST, DENSE, and Freiburg Thermal.