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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 thisdomain, notably the detection of small objects, managing densely packedelements, and accounting for diverse orientations. We present an in-depthevaluation of an object detection model that integrates the Large SelectiveKernel Network (LSKNet)as its backbone with the DiffusionDet head, utilizingthe iSAID dataset for empirical analysis. Our approach encompasses theintroduction of novel methodologies and extensive ablation studies. Thesestudies critically assess various aspects such as loss functions, boxregression techniques, and classification strategies to refine the model'sprecision in object detection. The paper details the experimental applicationof the LSKNet backbone in synergy with the DiffusionDet heads, a combinationtailored to meet the specific challenges in aerial image object detection. Thefindings of this research indicate a substantial enhancement in the model'sperformance, especially in the accuracy-time tradeoff. The proposed modelachieves a mean average precision (MAP) of approximately 45.7%, which is asignificant improvement, outperforming the RCNN model by 4.7% on the samedataset. This advancement underscores the effectiveness of the proposedmodifications and sets a new benchmark in aerial image analysis, paving the wayfor more accurate and efficient object detection methodologies. The code ispublicly available at https://github.com/SashaMatsun/LSKDiffDet

Innovative Horizons in Aerial Imagery: LSKNet Meets DiffusionDet for Advanced Object Detection