The field of object detection is witnessing significant developments, with a focus on addressing long-standing challenges such as feature extraction, scale variation, and real-time processing. Researchers are exploring novel network architectures, fusion techniques, and refinement methods to enhance detection accuracy and efficiency. Notably, the integration of hierarchical feature aggregation, dynamic dual fusion, and density-oriented feature-query manipulation is leading to state-of-the-art performance in various object detection tasks. Furthermore, the application of contour-aware and saliency priors embedding networks is improving the detection of small targets in infrared images. Overall, these advancements are paving the way for more accurate and efficient object detection systems. Noteworthy papers include: MDDFNet, which proposes a dynamic dual fusion network for traffic sign detection, and Dome-DETR, which introduces a density-oriented feature-query manipulation framework for efficient tiny object detection. CGTrack and DRRNet also demonstrate state-of-the-art performance in UAV tracking and camouflaged object detection, respectively.