## 1 Introduction
[10], execute both classification and regression tasks in a single step, thus boosting detection speed, albeit at the cost of a slight reduction in accuracy compared to Faster R-CNN. [PERSON] et al. [11] introduced the single-shot multibox detector (SSD), which leverages features from various layers to enhance feature extraction and achieve greater accuracy than YOLO. YOLOv3, presented by [PERSON] and [PERSON] [12], incorporated the feature pyramid network (FPN) [13] with multi-scale features to enhance accuracy in detecting small objects. [PERSON] et al. [14] proposed YOLOv4, which employed the CSPDarkNet as the backbone, boasting increased parameters and utilizing the spatial pooling layer (SPP) to expand the receptive field of the backbone network, ultimately leading to superior performance. [PERSON] et al. [15] adopted the Transformer architecture for object detection, eliminating the requirement for anchor boxes and introducing a direct set-based prediction approach. This work demonstrated the potential of Transformers in computer vision tasks beyond their traditional application in natural language processing. This study addresses the imperative need for enhancing the performance of current object detection methodologies and introduces improvements in the accuracy of image object detection tasks through the utilization of computer vision techniques such as feature pyramids. The proposed approach introduces an object detection methodology founded upon an augmented feature pyramid network. This approach specifically targets two principal limitations encountered in prevailing image object detection techniques. Firstly, it addresses the challenge of inadequate object detection across various scales by incorporating an augmented feature pyramid network to extract multi-scale features. This ameliorates the issue of significant information loss in top-level features during the fusion of multi-scale features in conventional methods, resulting in superior-quality multi-scale information. Secondly, the method tackles the limitation of commonly employed focal classification loss functions in object detection models, which tend to overly emphasize the classification features of intricate samples, leading to delayed model convergence. This is mitigated by refining the classification loss function to align more effectively with the attributes of the classification task, rendering it more proficient in accurate classification based on real-world conditions. The final experimental section encompasses diverse assessments, including ablation experiments conducted on the COCO2017 dataset and performance comparisons with 14 existing object detection algorithms on four widely recognized publicly accessible object detection datasets. The consistently observed outcomes of these experiments underscore the effectiveness of the proposed approach, showcasing higher detection accuracy in comparison to established baseline image object detection methods across these extensively utilized object detection datasets. In today's data-driven world, the role of images and videos as intuitive vehicles for transmitting information has taken on unprecedented significance. With the dawn of the AI era, computer vision techniques, particularly those grounded in deep learning, have enabled the autonomous and precise identification of objects within visual media. This technological advancement has propelled image and video analysis to the forefront of contemporary research, ushering in transformative possibilities across various domains. Our empirical findings not only substantiate the effectiveness of our approach but also establish it as a promising solution for discerning multi-scale objects within images and videos. Importantly, our research aligns with the quest for safer and more reliable autonomous driving systems, as it contributes to the evolving landscape of computer vision and object detection, directly impacting the future of mobility and security.