The demand of applying semantic segmentation model on mobile devices has been increasing rapidly. Current state-of-the-art networks have enormous amount of parameters hence unsuitable for mobile devices, while other small memory footprint models ignore the inherent characteristic of semantic segmentation. To tackle this problem, we propose a novel Context Guided Network (CGNet), which is a light-weight network for semantic segmentation on mobile devices. We first propose the Context Guided (CG) block, which learns the joint feature of both local feature and surrounding context, and further improves the joint feature with the global context. Based on the CG block, we develop Context Guided Network (CGNet), which captures contextual information in all stages of the network and is specially tailored for increasing segmentation accuracy. CGNet is also elaborately designed to reduce the number of parameters and save memory footprint. Under an equivalent number of parameters, the proposed CGNet significantly outperforms existing segmentation networks. Extensive experiments on Cityscapes and CamVid datasets verify the effectiveness of the proposed approach. Specifically, without any post-processing, CGNet achieves 64.8 mean IoU on Cityscapes with less than 0.5 M parameters, and has a frame-rate of 50 fps on one NVIDIA Tesla K80 card for 2048 @math 1024 high-resolution images. The source code for the complete system are publicly available.
Small semantic segmentation models require making a good trade-off on accuracy and model parameters or memory footprint. ENet @cite_43 proposes to discard the last stage of the model and shows that semantic segmentation is feasible on embedded devices. However, ICNet @cite_33 proposes a compressed-PSPNet-based image cascade network to speed up the semantic segmentation. More recent ESPNet @cite_13 introduces a fast and efficient convolutional network for semantic segmentation of high-resolution images under resource constraints. Most of them follow the design principles of image classification, which makes them have poor segmentation accuracy.
@cite_43 @cite_13 @cite_33
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The ability to perform pixel-wise semantic segmentation in real-time is of paramount importance in practical mobile applications. Recent deep neural networks aimed at this task have the disadvantage of requiring a large number of floating point operations and have long run-times that hinder their usability. In this paper, we propose a novel deep neural network architecture named ENet (efficient neural network), created specifically for tasks requiring low latency operation. ENet is up to 18x faster, requires 75x less FLOPs, has 79x less parameters, and provides similar or better accuracy to existing models. We have tested it on CamVid, Cityscapes and SUN datasets and report on comparisons with existing state-of-the-art methods, and the trade-offs between accuracy and processing time of a network. We present performance measurements of the proposed architecture on embedded systems and suggest possible software improvements that could make ENet even faster. In this paper we present a novel framework for geolocalizing Unmanned Aerial Vehicles (UAVs) using only their onboard camera. The framework exploits the abundance of satellite imagery, along with established computer vision and deep learning methods, to locate the UAV in a satellite imagery map. It utilizes the contextual information extracted from the scene to attain increased geolocalization accuracy and enable navigation without the use of a Global Positioning System (GPS), which is advantageous in GPS-denied environments and provides additional enhancement to existing GPS-based systems. The framework inputs two images at a time, one captured using a UAV-mounted downlooking camera, and the other synthetically generated from the satellite map based on the UAV location within the map. Local features are extracted and used to register both images, a process that is performed recurrently to relate UAV motion to its actual map position, hence performing preliminary localization. A semantic shape matching algorithm is subsequently applied to extract and match meaningful shape information from both images, and use this information to improve localization accuracy. The framework is evaluated on two different datasets representing different geographical regions. Obtained results demonstrate the viability of proposed method and that the utilization of visual information can offer a promising approach for unconstrained UAV navigation and enable the aerial platform to be self-aware of its surroundings thus opening up new application domains or enhancing existing ones. We focus on the challenging task of real-time semantic segmentation in this paper. It finds many practical applications and yet is with fundamental difficulty of reducing a large portion of computation for pixel-wise label inference. We propose an image cascade network (ICNet) that incorporates multi-resolution branches under proper label guidance to address this challenge. We provide in-depth analysis of our framework and introduce the cascade feature fusion unit to quickly achieve high-quality segmentation. Our system yields real-time inference on a single GPU card with decent quality results evaluated on challenging datasets like Cityscapes, CamVid and COCO-Stuff.