Foreign object debris (FOD) can cause serious accidents and economic loss. Synthetic aperture radar (SAR) aboard unmanned aerial systems (UAS) can provide high-resolution images independent of light and weather conditions. In this paper, a FOD detection model is proposed. It is based on the popular neural network model known as You Only Look Once (YOLO). FODs are small objects with few pixels. For this reason, an additional head is inserted into YOLOv8 to enhance the ability to detect small objects. Moreover, sliceassisted hyper-inference (SAHI) is employed during the prediction stage to further improve the detection rate. Finally, the model was trained using real experimental data obtained from UAS-borne SAR, and tested on unseen images. The results demonstrate that the proposed model can effectively and efficiently detect FOD with a detection rate of 83.3%.

A CUSTOMIZED NEURAL NETWORK MODEL FOR DETECTING FOREIGN OBJECT DEBRIS IN UAS-BORNE SAR IMAGES / Jingfeng Shan, Lapo Miccinesi, Alessandra Beni, Luca Bigazzi, Massimiliano Pieraccini. - ELETTRONICO. - (In corso di stampa), pp. 1-4. (Intervento presentato al convegno International Geoscience and Remote Sensing Symposium (IGARSS), 2025).

A CUSTOMIZED NEURAL NETWORK MODEL FOR DETECTING FOREIGN OBJECT DEBRIS IN UAS-BORNE SAR IMAGES

Jingfeng Shan;Lapo Miccinesi;Alessandra Beni;Luca Bigazzi;Massimiliano Pieraccini
In corso di stampa

Abstract

Foreign object debris (FOD) can cause serious accidents and economic loss. Synthetic aperture radar (SAR) aboard unmanned aerial systems (UAS) can provide high-resolution images independent of light and weather conditions. In this paper, a FOD detection model is proposed. It is based on the popular neural network model known as You Only Look Once (YOLO). FODs are small objects with few pixels. For this reason, an additional head is inserted into YOLOv8 to enhance the ability to detect small objects. Moreover, sliceassisted hyper-inference (SAHI) is employed during the prediction stage to further improve the detection rate. Finally, the model was trained using real experimental data obtained from UAS-borne SAR, and tested on unseen images. The results demonstrate that the proposed model can effectively and efficiently detect FOD with a detection rate of 83.3%.
In corso di stampa
International Geoscience and Remote Sensing Symposium (IGARSS), 2025
International Geoscience and Remote Sensing Symposium (IGARSS), 2025
Goal 9: Industry, Innovation, and Infrastructure
Jingfeng Shan, Lapo Miccinesi, Alessandra Beni, Luca Bigazzi, Massimiliano Pieraccini
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Utilizza questo identificatore per citare o creare un link a questa risorsa: https://hdl.handle.net/2158/1415892
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