The research advancements have made the neural network algorithms deployed in the autonomous vehicle to perceive the surrounding. The standard exteroceptive sensors that are utilized for the perception of the environment are cameras and Lidar. Therefore, the neural network algorithms developed using these exteroceptive sensors have provided the necessary solution for the autonomous vehicle's perception. One major drawback of these exteroceptive sensors is their operability in adverse weather conditions, for instance, low illumination and night conditions. The useability and affordability of thermal cameras in the sensor suite of the autonomous vehicle provide the necessary improvement in the autonomous vehicle's perception in adverse weather conditions. The semantics of the environment benefits the robust perception, which can be achieved by segmenting different objects in the scene. In this work, we have employed the thermal camera for semantic segmentation. We have designed an attention-based Recurrent Convolution Network (RCNN) encoder-decoder architecture named ARTSeg for thermal semantic segmentation. The main contribution of this work is the design of encoder-decoder architecture, which employ units of RCNN for each encoder and decoder block. Furthermore, additive attention is employed in the decoder module to retain high-resolution features and improve the localization of features. The efficacy of the proposed method is evaluated on the available public dataset, showing better performance with other state-of-the-art methods in mean intersection over union (IoU).
翻译:研究进展使得在自主载体中部署的神经网络算法能够感知周围环境。用于感知环境的标准外向感应器是照相机和利达尔。因此,利用这些外向感应器开发的神经网络算法为自动载体感知提供了必要的解决办法。这些外向感应器的一个主要缺陷是其在恶劣天气条件下的可操作性,例如低照明和夜间条件;自主载体传感器套件中热摄像头的可操作性和可负担性为自动载体在恶劣天气条件下的感知提供了必要的改进。环境的语义有利于通过对场景中不同物体进行分解而实现的强感。在这项工作中,我们使用了热感应摄像头来进行静感分解。我们设计了一个以注意力为基础的共振动网络(RCNN)编码解解码结构,名为ARTSeg,用于热静电解分解。这项工作的主要贡献是设计解器-解码结构结构,在每种电解特性中采用RCNNE的单位。在每种内采用高分辨率模块和低效制成。