Local Innovation System in Northern Finland–Case Renewable Energy Solutions Pilots in Oulu

To respond to the dynamics of urban surroundings, enhancing innovativeness in the urban environment has become increasingly important. The major challenge with innovations in urban environment is that many of them do not diffuse easily. The paper identifies the challenges related to urban innovation processes and their scaling-up, using the renewable energy solution pilot project as an example case. This is done by discussing the position and maturity of Renewable Energy Solutions in City Areas (RESCA) in an innovation typology context, and by assessing performed actions aimed at boosting the innovativeness in the case project. Results emphasized the essential role of the local building administration as a proactive stakeholder who started open-mindedly to address old-fashioned, inefficient and dominant practices of the construction industry. Another innovation hot-spot was that market actors needed to collaborate, take steps and present their ideas in order to find, implement and pilot the emerging solutions and innovations

3D Object Detection Algorithm Based on the Reconstruction of Sparse Point Clouds in the Viewing Frustum

In response to the problem that the detection precision of the current 3D object detection algorithm is low when the object is severely occluded, this study proposes an object detection algorithm based on the reconstruction of sparse point clouds in the viewing frustum. The algorithm obtains more local feature information of the sparse point clouds in the viewing frustum through dimensional expansion, performs the fusion of local and global feature information of the point cloud data to obtain point cloud data with more complete semantic information, and then applies the obtained data to the 3D object detection task. The experimental results show that the precision of object detection in both 3D view and BEV (Bird’s Eye View) can be improved effectively through the algorithm, especially object detection of moderate and hard levels when the object is severely occluded. In the 3D view, the average precision of the 3D detection of cars, pedestrians, and cyclists at a moderate level can be increased by 7.1p.p., 16.39p.p., and 5.42p.p., respectively; in BEV, the average precision of the 3D detection of car, pedestrians, and cyclists at hard level can be increased by 6.51p.p., 16.57p.p., and 7.18p.p., respectively, thus indicating the effectiveness of the algorithm.© 2022 Xing Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.fi=vertaisarvioitu|en=peerReviewed