… object detection in autonomous driving scenarios are discussed in this section. Section IV
introduces deep learning (DL) and the factors that make DL a powerful technique in computer …

… object detection architectures along with some modifications and useful tricks to improve
detection … progress of object detection, there are still many open issues for the future work. …

… In addition, we identify major open issues regarding the … Although we restrict our attention
to object detection in still … 3) We present and discuss open issues at the problemlevel and …

… music scores or were built to detect only a subset of the … object detector for music symbols
that is capable of detecting … detect music objects with a mean average precision of over 80%. …

… for object detection as deep learning (DL) techniques have advanced. As a result, numerous
approaches for object detection … strategies, classical object detection architectures, complex …

… Finally, we discuss several open issues of SOD and outline future research directions. All
the saliency prediction maps, our constructed dataset with annotations, and codes for …

… The domain adaptation is introduced for learning domain-agnostic features for alleviating
the domain shift issues caused by carrying out object detection in adverse weather. So to …

… , open issues, and research gaps in 3D object detection. 4) We summarize commonly used
sensors and new datasets for 3D object detection … for 3D object detection are summarized in …

… A model that provides high-performance and accuracy to real-world data would offer
solutions to pressing issues such as surveillance, face-detection and most of all, autonomous …

… do not address other issues such as covariate shift and mismatch in object category … object
detector with unconstrained unlabeled images – Open-Set Semi-Supervised Object Detection …