The field of object detection is moving towards more accurate and efficient detection of small targets in various environments, including underwater, medical, and field settings. Researchers are improving existing models, such as YOLO, by incorporating advanced features like attention mechanisms, self-supervised learning, and multi-scale feature extraction. These innovations are leading to significant performance gains and increased applicability in real-world applications. Notable papers in this area include:

• Underwater Waste Detection Using Deep Learning, which demonstrated the effectiveness of YOLOv8 in detecting waste materials in underwater environments.
• ABCD: Automatic Blood Cell Detection via Attention-Guided Improved YOLOX, which proposed an improved YOLOX model for detecting blood cells in microscopic images.
• An Improved YOLOv8 Approach for Small Target Detection of Rice Spikelet Flowering in Field Environments, which developed an improved YOLOv8 model for detecting rice spikelet flowering in field environments.
• YOLO-ROC: A High-Precision and Ultra-Lightweight Model for Real-Time Road Damage Detection, which introduced a lightweight and high-precision model for real-time road damage detection.