Advancing Image Object Detection: Enhanced Feature Pyramid Network and Gradient Density Loss for Improved Performance

Abstract

In the era of artificial intelligence, the significance of images and videos as intuitive conveyors of information cannot be overstated. Computer vision techniques rooted in deep learning have revolutionized our ability to autonomously and accurately identify objects within visual media, making them a focal point of contemporary research. This study addresses the pivotal role of image object detection, particularly in the contexts of autonomous driving and security surveillance, by presenting an in-depth exploration of this field with a focus on enhancing the feature pyramid network. One of the key challenges in existing object detection methodologies lies in mitigating information loss caused by multi-scale feature fusion. To tackle this issue, we propose the enhanced feature pyramid, which adeptly amalgamates features extracted across different scales. This strategic enhancement effectively curbs information attrition across various layers, thereby strengthening the feature extraction capabilities of the foundational network. Furthermore, we confront the issue of excessive classification loss in image object detection tasks by introducing the gradient density loss function, designed to mitigate classification discrepancies. Empirical results unequivocally demonstrate the efficacy of our approach in enhancing the detection of multi-scale objects within images. When evaluated across benchmark datasets, including MS COCO 2017, MS COCO 2014, Pascal VOC 2007, and Pascal VOC 2012, our method achieves impressive average precision scores of 39.4%, 42.0%, 51.5%, and 49.9%, respectively. This performance clearly outperforms alternative state-of-the-art methods in the field. This research not only contributes to the evolving landscape of computer vision and object detection but also has practical implications for a wide range of applications, aligning with the transformative trends in the automotive industry and security technologies.