Detecting Occluded and Dense Trees in Urban Terrestrial Views With a High-Quality Tree Detection Dataset

Urban trees are often densely planted along the two sides of a street. When observing these trees from a fixed view, they are inevitably occluded with each other and the passing vehicles. The high density and occlusion of urban tree scenes significantly degrade the performance of object detectors. This article raises an intriguing learning-related question-if a module is developed to enable the network to adaptively cope with occluded and unoccluded regions while enhancing its feature extraction capabilities, can the performance of a cutting-edge detection model be improved? To answer it, a lightweight yet effective object detection network is proposed for discerning occluded and dense urban trees, called occluded and dense-urban tree detection network (OD-UTDNet). The main contribution is a newly designed dilated attention cross stage partial (DACSP) module. DACSP can expand the fields of view of OD-UTDNet for paying more attention to the unoccluded region, while enhancing the network's feature extraction ability in the occluded region. This work further explores both the self-calibrated (SC) convolution module and GFocal loss, which enhance OD-UTDNet's ability to resolve the challenging problem of high densities and occlusions. Finally, to facilitate the detection task of urban trees, a high-quality urban tree detection dataset is established, named Urban Tree Detection (UTD); to our knowledge, this is the first time. Extensive experiments show clear improvements of the proposed OD-UTDNet over 12 representative object detectors on UTD. The code and dataset are available at https://github.com/yz-wang/OD-UTDNet.