The MaSTr1325 dataset for training deep USV obstacle detection models 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 datahungry deep CNNs. This paper addresses this issue by presenting MaSTr1325, a marine semantic segmentation training dataset tailored for development of obstacle detection methods in smallsized 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 perpixel 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.

Utilization of the open source datasets for semantic segmentation in automotive vision Great advancements in deeplearningbased machine vision are boosting the automotive industry potential to accurately recognize vehicle environment. The need for high quality datasets is of critical importance when training neural networks which are used to detect and classify objects in front of the vehicle. Available open source datasets for semantic segmentation can be used by a wide community of researchers to develop next generation selfdriving functions. Those datasets have severe limitations such as class imbalance, unobserved objects, erroneous labelling and limited number of covered scenarios. In this paper, we propose a sequence of steps to combine and manipulate existing open source datasets to maximize the inference performance. Those steps include relabeling with outlier removal, classdriven balancing of validation and training datasets, as well as targeted image manipulation for scarce classes. Our evaluation indicates the improved inference accuracy when compared to the usage of most common open source datasets.