YOLOv8s-DroneNet: Small Object Detection Algorithm Based on Feature Selection and ISIoU

Object detection plays a critical role in drone imagery analysis, especially in remote sensing applications where accurate and efficient detection of small objects is essential. Despite significant advancements in drone imagery detection, most models still struggle with small object detection due to challenges such as object size, complex backgrounds. To address these issues, we propose a robust detection model based on You Only Look Once (YOLO) that balances accuracy and efficiency. The model mainly contains several major innovation: feature selection pyramid network, Inner-Shape Intersection over Union (ISIoU) loss function and small object detection head. To overcome the limitations of traditional fusion methods in handling multi-level features, we introduce a Feature Selection Pyramid Network integrated into the Neck component, which preserves shallow feature details critical for detecting small objects. Additionally, recognizing that deep network structures often neglect or degrade small object features, we design a specialized small object detection head in the shallow layers to enhance detection accuracy for these challenging targets. To effectively model both local and global dependencies, we introduce a Conv-Former module that simulates Transformer mechanisms using a convolutional structure, thereby improving feature enhancement. Furthermore, we employ ISIoU to address object imbalance and scale variation This approach accelerates model conver-gence and improves regression accuracy. Experimental results show that, compared to the baseline model, the proposed method significantly improves small object detection performance on the VisDrone2019 dataset, with mAP@50 increasing by 4.9% and mAP@50-95 rising by 6.7%. This model also outperforms other state-of-the-art algorithms, demonstrating its reliability and effectiveness in both small object detection and remote sensing image fusion tasks.