MAVNet: an Effective Semantic Segmentation Micro-Network for MAV-based Tasks

Real-time image semantic segmentation is an essential capability to enhance robot autonomy and improve human situational awareness. In this paper, we present MAVNet, a novel deep neural network approach for semantic segmentation suitable for small scale Micro Aerial Vehicles (MAVs). Our approach is compatible with the size, weight, and power(SWaP) constraints typical of small scale MAVs, which can only employ small processing units and GPUs. These units have typically limited computational capacity, which has to be concurrently shared with other real time performance tasks such as visual odometry and path planning. Our proposed solution MAVNet, is a fast and compact network inspired by ERFNet and features about 400 times fewer parameters in comparison. Experimental results on multiple datasets validate our proposed approach. Additionally, comparisons with other state of the art approaches show that our solution outperforms theirs in terms of speed and accuracy achieving up to 48 FPS on an NVIDIA 1080Ti and 9 FPS on the NVIDIA Jetson Xavier when processing high resolution imagery. Our algorithm and datasets are made publicly available.

U-Net for MAV-based Penstock Inspection: an Investigation of Focal Loss in Multi-class Segmentation for Corrosion Identification

Periodical inspection and maintenance of critical infrastructure such as dams, penstocks, and locks are of significant importance to prevent catastrophic failures. Conventional manual inspection methods require inspectors to climb along a penstock to spot corrosion, rust and crack formation which is unsafe, labor-intensive, and requires intensive training. This work presents an alternative approach using a Micro Aerial Vehicle (MAV) that autonomously flies to collect imagery which is then fed into a pretrained deep-learning model to identify corrosion. Our simplified U-Net trained with less than 40 image samples can do inference at 12 fps on a single GPU. We analyze different loss functions to solve the class imbalance problem, followed by a discussion on choosing proper metrics and weights for object classes. Results obtained with the dataset collected from Center Hill Dam, TN show that focal loss function, combined with a proper set of class weights yield better segmentation results than the base loss, Softmax cross entropy. Our method can be used in combination with planning algorithm to offer a complete, safe and cost-efficient solution to autonomous infrastructure inspection.