The field of object detection is moving towards developing more efficient and accurate models that can operate in real-time, even in challenging environments such as low-light conditions, complex scenes, and resource-constrained platforms. Researchers are exploring innovative architectures, loss functions, and feature fusion methods to improve the detection of small objects, reduce computational costs, and enhance deployment readiness. Notable advancements include the use of hierarchical feature fusion, lightweight models, and novel loss functions to address class imbalance, thermal noise, and occlusion. These developments have significant implications for applications such as urban object detection, UAV photography, and forestry pest detection. Noteworthy papers include: MS-YOLO, which introduces a novel loss function and replaces the traditional backbone with a more efficient one, reducing computational overhead while sustaining high accuracy. HierLight-YOLO, which proposes a hierarchical feature fusion method and lightweight modules to enhance small object detection accuracy. YOLO26, which presents key architectural enhancements and performance benchmarking for real-time object detection, highlighting superior efficiency and accuracy. Forestpest-YOLO, which introduces a synergistic trio of innovations to detect small forestry pests in complex environments, achieving state-of-the-art performance.