Abstract: Object detection is one of the pivotal technologies within the field of computer vision.Its objective is to pinpoint the locations of objects and recognize their affiliated classes within images or videos,finding extensive applications in domains like intelligent transportation,security monitoring,and industrial inspection.The YOLOv8 object detection approach has attained remarka-ble achievements in both detection precision and real-time responsiveness.Nevertheless,it encounters formidable challenges when dealing with complex background interferences,small object detection,and occlusions,often resulting in false positives or missed detections.To augment the accuracy of object detection,an object detection algorithm based on YOLOv8 enhancement is proposed,and the corresponding application specification are discussed.On the technical front,a spatial attention mechanism is incorporated into the backbone network,bolstering the feature extraction capabilities for key objects.Secondly,an adaptive feature fusion module is devised to enhance the integration proficiency of multi-scale feature maps.Subsequently,data augmentation techniques and transfer learning strategies are employed to effectively tackle the problems of sample imbalance and restricted object quantities in the dataset.Then,via a dynamic weight adjustment mechanism for bounding box regression loss and classification loss,the predictive accuracy is further elevated.Ultimately,extensive experiments conducted on five datasets,namely COCO,PASCAL VOC,Cityscapes,KITTI and VisDrone,validate that the proposed method outperforms other SOTA methods in terms of detection accuracy and operational speed.Notably in complex scenarios,small object detection,and occlusion circumstances,the robustness and accuracy of the model are conspicuously boosted.At the application specification level,with the aim of mitigating the security risks to personal image privacy data arising from the application of large-scale object detection algorithms,it is imperative to formulate comprehensive application norms in aspects such as law,ethics,and technology,so as to promote the progress of technology to closely align with the needs of social development.

Key words: YOLOv8, Object detection, Spatial attention, Adaptive feature fusion, Complex scenes, Applicationnorms