Automating inspection of critical infrastructure such as sewer systems will help utilities optimize maintenance and replacement schedules. The current inspection process consists of manual reviews of video as an operator controls a sewer inspection vehicle remotely. The process is slow, labor-intensive, and expensive and presents a huge potential for automation. With this work, we address a central component of the next generation of robotic inspection of sewers, namely the choice of 3D sensing technology. We investigate three prominent techniques for 3D vision: passive stereo, active stereo, and time-of-flight (ToF). The Realsense D435 camera is chosen as the representative of the first two techniques wheres the PMD CamBoard pico flexx represents ToF. The 3D reconstruction performance of the sensors is assessed in both a laboratory setup and in an outdoor above-ground setup. The acquired point clouds from the sensors are compared with reference 3D models using the cloud-to-mesh metric. The reconstruction performance of the sensors is tested with respect to different illuminance levels and different levels of water in the pipes. The results of the tests show that the ToF-based point cloud from the pico flexx is superior to the output of the active and passive stereo cameras.
Real-world single image Super-Resolution (SR) aims to enhance the resolution and reconstruct High-Resolution (HR) details of real Low-Resolution (LR) images. This is different from the traditional SR setting, where the LR images are synthetically created, typically with bicubic downsampling. As the degradation process for real-world LR images are highly complex, SR of such images is much more challenging. Recent promising approaches to solve the Real-World Super-Resolution (RWSR) problem include the use of domain adaptation to create realistic trainingpairs, and self-learning based methods which learn an image specific SR model at test time. However, as domain adaptation is an inherently challenging problem in itself, SR models based solely on this approach are limited by the domain gap. In contrast, while self-learning based methods remove the need for paired-training data by utilizing internal information in the LR image, these methods come with the cost of slow prediction times. This paper proposes a novel framework, Semantic Segmentation Guided Real-World Super-Resolution (SSG-RWSR), which uses an auxiliary semantic segmentation network to guide the SR learning. This results in noise-free reconstructions with accurate object boundaries, and enables training on real LR images. The latter allows our SR network to adapt to the image specific degradations, without Ground-Truth (GT) reference images. We support the guidance with domain adaptation to faithfully reconstruct realistic textures, and ensure color consistency. We evaluate our proposed method on two public available datasets, and present State-of-the-Art results in terms of perceptual image quality on both real and synthesized LR images.
In thermal video security monitoring the reliability of deployed systems rely on having varied training data that can effectively generalize and have consistent performance in the deployed context. However, for security monitoring of an outdoor environment the amount of variation introduced to the imaging system would require extensive annotated data to fully cover for training and evaluation. To this end we designed and ran a challenge to stimulate research towards alleviating the impact of concept drift on object detection performance. We used an extension of the Long-Term Thermal Imaging Dataset, composed of thermal data acquired from 14th May 2020 to 30th of April 2021, with a total of 1689 2-minute clips with bounding-box annotations for 4 different categories. The data covers a wide range of different weather conditions and object densities with the goal of measuring the thermal drift over time, from the coldest day/week/month of the dataset. The challenge attracted 184 registered participants, which was considered a success from the perspective of the organizers. While participants managed to achieve higher mAP when compared to a baseline, concept drift remains a strongly impactful factor. This work describes the challenge design, the adopted dataset and obtained results, as well as discuss top-winning solutions and future directions on the topic.
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