Pushing the efficiency of StereoNet: exploiting spatial sparsity

Current CNN-based stereo matching methods have demonstrated superior performance compared to traditional stereo matching methods. However, mapping these algorithms into embedded devices, which exhibit limited compute resources, and achieving high performance is a challenging task due to the high computational complexity of the CNN-based methods. The recently proposed StereoNet network, achieves disparity estimation with reduced complexity, whereas performance does not greatly deteriorate. Towards pushing this performance to complexity trade-off further, we propose an optimization applied to StereoNet that adapts the computations to the input data, steering the computations to the regions of the input that would benefit from the application of the CNN-based stereo matching algorithm, where the rest of the input is processed by a traditional, less computationally demanding method. Key to the proposed methodology is the introduction of a lightweight CNN that predicts the importance of r efining a region of the input to the quality of the final disparity map, allowing the system to trade-off computational complexity for disparity error on-demand, enabling the application of these methods to embedded systems with real-time requirements

WTA/TLA: A UAV-captured dataset for semantic segmentation of energy infrastructure

Automated inspection of energy infrastructure with Unmanned Aerial Vehicles (UAVs) is becoming increasingly important, exhibiting significant advantages over manual inspection, including improved scalability, cost/time effectiveness, and risks reduction. Although recent technological advancements enabled the collection of an abundance of vision data from UAVs’ sensors, significant efforts are still required from experts to interpret manually the collected data and assess the condition of energy infrastructure. Thus, semantic understanding of vision data collected from UAVs during inspection is a critical prerequisite for performing autonomous robotic tasks. However, the lack of labeled data introduces challenges and limitations in evaluating the performance of semantic prediction algorithms. To this end, we release two novel semantic datasets (WTA and TLA) of aerial images captured from power transmission networks and wind turbine farms, collected during real inspection scenarios with UAVs. We also propose modifications to existing state-of-the-art semantic segmentation CNNs to achieve improved trade-off between accuracy and computational complexity. Qualitative and quantitative experiments demonstrate both the challenging properties of the provided dataset and the effectiveness of the proposed networks in this domain.The dataset is available at: https://github.com/gzamps/wta_tla_dataset