Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection
This work addresses challenges like detecting small objects in aerial images for applications such as remote sensing, though it appears incremental as it combines existing methods.
The study tackled object detection in aerial imagery by integrating LSKNet as a backbone with DiffusionDet heads, achieving a mean average precision of 45.7%, which outperformed RCNN by 4.7% on the iSAID dataset.
In the realm of aerial image analysis, object detection plays a pivotal role, with significant implications for areas such as remote sensing, urban planning, and disaster management. This study addresses the inherent challenges in this domain, notably the detection of small objects, managing densely packed elements, and accounting for diverse orientations. We present an in-depth evaluation of an object detection model that integrates the Large Selective Kernel Network (LSKNet)as its backbone with the DiffusionDet head, utilizing the iSAID dataset for empirical analysis. Our approach encompasses the introduction of novel methodologies and extensive ablation studies. These studies critically assess various aspects such as loss functions, box regression techniques, and classification strategies to refine the model's precision in object detection. The paper details the experimental application of the LSKNet backbone in synergy with the DiffusionDet heads, a combination tailored to meet the specific challenges in aerial image object detection. The findings of this research indicate a substantial enhancement in the model's performance, especially in the accuracy-time tradeoff. The proposed model achieves a mean average precision (MAP) of approximately 45.7%, which is a significant improvement, outperforming the RCNN model by 4.7% on the same dataset. This advancement underscores the effectiveness of the proposed modifications and sets a new benchmark in aerial image analysis, paving the way for more accurate and efficient object detection methodologies. The code is publicly available at https://github.com/SashaMatsun/LSKDiffDet