Work

Tiny Foreign Object Debris Detection Considering Multi‐Channel Information Fusion and Gaussian Distribution of Pixels

Year: 2025

Type: article

Abstract: ABSTRACT Foreign object debris (FOD) detection is critical to aircraft safety, but existing visual algorithms have difficulty in detecting tiny objects and in low‐light conditions. FOD detection in low‐light conditions can be achieved using laser line‐scan cameras, but there is still a lot of room for research on how to better use the multi‐channel images obtained by the camera. To address these issues, this paper proposes a tiny FOD detection algorithm (TFD‐Net) suitable for laser line‐scan cameras and visible light cameras, along with a new multi‐channel information fusion (NMIF) method based on laser line‐scan camera image features. The proposed TFD‐Net is designed specifically for tiny FOD with three key parts: a loss function based on two‐dimensional (2D) Gaussian distribution, a multi‐scale detection head and an improved pooling module. These designs can effectively extract tiny FOD features and achieve high‐precision detection. The proposed NMIF makes better use of the three‐channel image features acquired by the laser line‐scan camera, improving the effectiveness of the laser line‐scan camera in FOD detection significantly.

Source: IET Cyber-Systems and Robotics

DOI: 10.1049/csy2.70039

Authors: Zhicong Lu, Guoliang Liu, Changteng Shi, Yichao Cao, Dongxuan Li, Guohui Tian

Institution: Shandong University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Natural Science Foundation of Shandong Province

Awards: G4673741603, G7107184510