Recent advancements in object detection are increasingly focused on enhancing efficiency and adaptability in real-time applications. New methodologies, particularly those based on the Detection Transformer (DETR) framework, are addressing traditional challenges such as query utilization and computational overhead. For instance, recent work has introduced matching-free training schemes that eliminate the need for heuristic matching, significantly improving training speed and performance. Additionally, innovations like RiO-DETR are enabling real-time detection of oriented objects, overcoming issues related to angle periodicity and search space complexity. The introduction of dynamic query generation in frameworks like PaQ-DETR is further refining the balance between accuracy and interpretability. Meanwhile, specialized solutions for small object detection in UAV imagery, such as CollabOD, are optimizing feature alignment and structural detail preservation. These developments not only promise to enhance the robustness of object detection systems but also open avenues for commercial applications in surveillance, autonomous vehicles, and robotics, where efficiency and accuracy are paramount.
Recent real-time detection transformers have gained popularity due to their simplicity and efficiency. However, these detectors do not explicitly model object rotation, especially in remote sensing im...
Recent DEtection TRansformer (DETR) based frameworks have achieved remarkable success in end-to-end object detection. However, the reliance on the Hungarian algorithm for bipartite matching between qu...
Identifying potential objects is critical for object recognition and analysis across various computer vision applications. Existing methods typically localize potential objects by relying on exemplar ...
Small object detection in unmanned aerial vehicle (UAV) imagery is challenging, mainly due to scale variation, structural detail degradation, and limited computational resources. In high-altitude scen...
Out-of-distribution (OOD) object detection is an important yet underexplored task. A reliable object detector should be able to handle OOD objects by localizing and correctly classifying them as OOD. ...
Detection Transformer (DETR) has redefined object detection by casting it as a set prediction task within an end-to-end framework. Despite its elegance, DETR and its variants still rely on fixed learn...
We present RiO-DETR: DETR for Real-time Oriented Object Detection, the first real-time oriented detection transformer to the best of our knowledge. Adapting DETR to oriented bounding boxes (OBBs) pose...
Although recent Open-Vocabulary Object Detection architectures, such as Grounding DINO, demonstrate strong zero-shot capabilities, their performance degrades significantly under domain shifts. Moreove...
Single-domain generalization is essential for object detection, particularly when training models on a single source domain and evaluating them on unseen target domains. Domain shifts, such as changes...
Object detection in video and image surveillance is a well-established yet rapidly evolving task, strongly influenced by recent deep learning advancements. This review summarises modern techniques by ...