The field of object detection and tracking is witnessing significant advancements, driven by the need for real-time processing and improved accuracy. Researchers are exploring innovative approaches to address challenges such as low-light conditions, small object detection, and efficient feature extraction. A key trend is the development of lightweight frameworks that can operate in real-time, leveraging techniques like feature reuse and attention mechanisms to enhance performance. Another area of focus is the integration of multi-scale information and contextual understanding to improve detection accuracy. Noteworthy papers include: RARE, which introduces a novel Attention Score Ranking Loss to prioritize accident-related objects and achieves state-of-the-art Average Precision. Cross-DINO, which incorporates a deep MLP network and a new loss function called Boost Loss to improve small object detection performance. SAMamba, which presents a novel framework integrating hierarchical feature learning with selective sequence modeling for infrared small target detection. CF-DETR, which proposes a coarse-to-fine Transformer architecture and a dedicated real-time scheduling framework to meet firm real-time deadlines and high accuracy requirements.