/tags/cnns/ Recent content in CNNs on ViCoS Lab Hugo en-us /publications/bovcon2020a/ Mon, 01 Jan 0001 00:00:00 +0000 /publications/bovcon2020a/ <p>Obstacle detection by semantic segmentation shows a great promise for autonomous navigation in unmanned surface vehicles (USV). However, existing methods suffer from poor estimation of the water edge in presence of visual ambiguities, poor detection of small obstacles and high false-positive rate on water reflections and wakes. We propose a new deep encoder-decoder architecture, a water-obstacle separation and refinement network (WaSR), to address these issues. Detection and water edge accuracy are improved by a novel decoder that gradually fuses inertial information from IMU with the visual features from the encoder. In addition, a novel loss function is designed to increase the separation between water and obstacle features early on in the network. Subsequently, the capacity of the remaining layers in the decoder is better utilised, leading to a significant reduction in false positives and increased true positives. Experimental results show that WaSR outperforms the current state-of-the-art by a large margin, yielding a 14% increase in F-measure over the second-best method.</p> /publications/bovcon2019the/ Mon, 01 Jan 0001 00:00:00 +0000 /publications/bovcon2019the/ <p>The progress of obstacle detection via semantic segmentation on unmanned surface vehicles (USVs) has been significantly lagging behind the developments in the related field of autonomous cars. The reason is the lack of large curated training datasets from USV domain required for development of data-hungry deep CNNs. This paper addresses this issue by presenting MaSTr1325, a marine semantic segmentation training dataset tailored for development of obstacle detection methods in small-sized coastal USVs. The dataset contains 1325 diverse images captured over a two year span with a real USV, covering a range of realistic conditions encountered in a coastal surveillance task. The images are per-pixel semantically labeled. The dataset exceeds previous attempts in this domain in size, scene complexity and domain realism. In addition, a dataset augmentation protocol is proposed to address slight appearance differences of the images in the training set and those in deployment. The accompanying experimental evaluation provides a detailed analysis of popular deep architectures, annotation accuracy and influence of the training set size. MaSTr1325 will be released to reaserch community to facilitate progress in obstacle detection for USVs.</p>