This paper consists of a bibliometric study that covers the topic of 3D object detection from 2022 until the present day. It employs various analysis approaches that shed light on the leading authors, affiliations, and countries within this research domain alongside the main themes of interest related to it. The findings revealed that China is the leading country in this domain given the fact that it is responsible for most of the scientific literature as well as being a host for the most productive universities and authors in terms of the number of publications. China is also responsible for initiating a significant number of collaborations with various nations around the world. The most basic theme related to this field is deep learning, along with autonomous driving, point cloud, robotics, and LiDAR. The work also includes an in-depth review that underlines some of the latest frameworks that took on various challenges regarding this topic, the improvement of object detection from point clouds, and training end-to-end fusion methods using both camera and LiDAR sensors, to name a few.

Deep Convolutional Neural Networks (DCNNs) are regarded as one of the foundations of computer vision due to their unparalleled ability to process visual data. This work explores the use of DCNNs to estimate the orientation of vehicles from a single 2D image. The experiments included 48 training scenarios encompassing four dataset variations and 12 models, followed by an evaluation of each one based on four key metrics. Overall the best-performing architecture was EfficientNet-B2, achieving an accuracy of 97.22% consistently across all dataset variations, demonstrating its robustness to preprocessing techniques. Additionally, ResNet18 delivered competitive results, achieving the highest recorded accuracy of 98.61% on the original dataset, while MobileNetV2 also performed exceptionally well on the augmented and no-background datasets, reaching 98.61%. EfficientNet-B5 initially underperformed on the original dataset but significantly improved with augmentation, achieving 97.22% accuracy. The study revealed that dataset preprocessing played a crucial role in model performance, with augmentation and background removal significantly boosting accuracy. The classification mistakes were further analyzed using SHAP values, highlighting the importance of specific car features, such as the front and rear sections, in determining orientation. Overall, the results confirmed that vehicle orientation estimation can be effectively approached as a classification problem. The ResNet family proved highly robust, while EfficientNet-B2 emerged as a strong contender due to its consistency. MobileNetV2's efficiency and strong performance make it a viable option for real-time applications. Future work should explore transformer-based architectures and evaluate model performance on real-world datasets with varying environmental conditions.