This study introduces YOLOv8-CAB, an enhanced version of the YOLOv8 model designed to improve the accuracy of object detection, particularly for small objects in varied image types. The proposed method incorporates the Context Attention Block (CAB) to effectively locate and identify small objects by leveraging multi-scale feature maps and iterative feedback. Additionally, the Coarse-to-Fine (C2F) block is modified to increase feature extraction capability without increasing model complexity. The Spatial Attention (SA) module is also enhanced to accelerate detection performance. Rigorous testing on the Common Objects in Context (COCO) dataset demonstrates that YOLOv8-CAB achieves a mean average precision (mAP) of 97%, representing a 1% improvement over conventional models. The study highlights the effectiveness of the proposed method in detecting small objects, setting a new benchmark for real-time object detection techniques.This study introduces YOLOv8-CAB, an enhanced version of the YOLOv8 model designed to improve the accuracy of object detection, particularly for small objects in varied image types. The proposed method incorporates the Context Attention Block (CAB) to effectively locate and identify small objects by leveraging multi-scale feature maps and iterative feedback. Additionally, the Coarse-to-Fine (C2F) block is modified to increase feature extraction capability without increasing model complexity. The Spatial Attention (SA) module is also enhanced to accelerate detection performance. Rigorous testing on the Common Objects in Context (COCO) dataset demonstrates that YOLOv8-CAB achieves a mean average precision (mAP) of 97%, representing a 1% improvement over conventional models. The study highlights the effectiveness of the proposed method in detecting small objects, setting a new benchmark for real-time object detection techniques.