Staircase detection in natural images has several applications in the context of robotics and visually impaired navigation. Previous works are mainly based on handcrafted feature extraction and supervised learning using fully annotated images. In this work we address the problem of staircase detection in weakly labeled natural images, using a novel Fully Convolutional neural Network (FCN), named LB-FCN light. The proposed network is an enhanced version of our recent Look-Behind FCN (LB-FCN), suitable for deployment on mobile and embedded devices. Its architecture features multi-scale feature extraction, depthwise separable convolutions and residual learning. To evaluate its computational and classification performance, we have created a weakly-labeled benchmark dataset from publicly available images. The results from the experimental evaluation of LB-FCN light indicate its advantageous performance over the relevant state-of-the-art architectures.