This study presents a novel approach to enhance the accuracy of the YOLOv8 model in object detection, focusing on improving the detection of small objects in varied image types. The proposed method incorporates the Context Attention Block (CAB) to effectively locate and identify small objects. Additionally, the feature extraction capability is improved without increasing model complexity by enhancing the Coarse-to-Fine (C2F) block. The Spatial Attention (SA) module is also modified to accelerate detection performance. The enhanced YOLOv8 model, named YOLOv8-CAB, emphasizes the detection of smaller objects by leveraging the CAB block to exploit multi-scale feature maps and iterative feedback, optimizing object detection mechanisms. Rigorous testing on the COCO dataset demonstrates the effectiveness of the proposed technique, achieving a mean average precision (mAP) of 97%, indicating a 1% improvement over conventional models. The study highlights the capabilities of the improved YOLOv8 method in detecting objects, representing a breakthrough in real-time object detection techniques. The YOLOv8-CAB model is designed to enhance small object detection by integrating the CAB block into the model's architecture, addressing the intricacies of detecting fine-scale objects without compromising real-time processing capabilities. Empirical evaluations on the COCO dataset show a 2.1% increase in mAP for objects under a certain size threshold compared to the baseline YOLOv8, outperforming contemporary models like NanoDet in speed and precision. These improvements enable the application of YOLOv8-CAB in scenarios where rapid and precise detection of small objects is critical, such as in medical diagnostics or disaster response. The proposed method includes modifications to the C2F module and the spatial attention module. The C2F module is expanded to enhance feature learning, while the spatial attention module is improved by incorporating the Selective Kernel (SK) attention mechanism. These modifications enhance small object detection and selectively highlight important spatial regions and channels. The YOLOv8-CAB algorithm is tested on the COCO dataset, demonstrating superior performance in detecting small and medium-sized objects. The model achieves a mean average precision of 97% for small object detection, indicating a significant improvement over conventional models. The study concludes that YOLOv8-CAB represents a notable advancement in small object detection, with potential for future applications in real-time object detection.This study presents a novel approach to enhance the accuracy of the YOLOv8 model in object detection, focusing on improving the detection of small objects in varied image types. The proposed method incorporates the Context Attention Block (CAB) to effectively locate and identify small objects. Additionally, the feature extraction capability is improved without increasing model complexity by enhancing the Coarse-to-Fine (C2F) block. The Spatial Attention (SA) module is also modified to accelerate detection performance. The enhanced YOLOv8 model, named YOLOv8-CAB, emphasizes the detection of smaller objects by leveraging the CAB block to exploit multi-scale feature maps and iterative feedback, optimizing object detection mechanisms. Rigorous testing on the COCO dataset demonstrates the effectiveness of the proposed technique, achieving a mean average precision (mAP) of 97%, indicating a 1% improvement over conventional models. The study highlights the capabilities of the improved YOLOv8 method in detecting objects, representing a breakthrough in real-time object detection techniques. The YOLOv8-CAB model is designed to enhance small object detection by integrating the CAB block into the model's architecture, addressing the intricacies of detecting fine-scale objects without compromising real-time processing capabilities. Empirical evaluations on the COCO dataset show a 2.1% increase in mAP for objects under a certain size threshold compared to the baseline YOLOv8, outperforming contemporary models like NanoDet in speed and precision. These improvements enable the application of YOLOv8-CAB in scenarios where rapid and precise detection of small objects is critical, such as in medical diagnostics or disaster response. The proposed method includes modifications to the C2F module and the spatial attention module. The C2F module is expanded to enhance feature learning, while the spatial attention module is improved by incorporating the Selective Kernel (SK) attention mechanism. These modifications enhance small object detection and selectively highlight important spatial regions and channels. The YOLOv8-CAB algorithm is tested on the COCO dataset, demonstrating superior performance in detecting small and medium-sized objects. The model achieves a mean average precision of 97% for small object detection, indicating a significant improvement over conventional models. The study concludes that YOLOv8-CAB represents a notable advancement in small object detection, with potential for future applications in real-time object detection.