# Small-Scale Foreign Object Debris Detection Using Deep Learning and Dual Light Modes
###### Abstract
When the target is tested with a small size or in low lighting conditions, such as during the night, visible light cannot effectively capture the image of the target. Many domestic and international researchers have proposed methods for the fusion and recognition of infrared and visible light images to address the challenge of detecting small targets in visible light images [14; 15; 16; 17]. Compared with existing radar detection systems, infrared imaging technology is less susceptible to interference from electronic devices and can compensate for the limitations of visible light detection systems in smoke, fog, and night vision scenarios. Currently, fusion strategies for dual light data can be categorized into data-level fusion, decision-level fusion, and feature-level fusion. The main idea of data-level fusion is to decompose infrared and visible images with different filters and to fuse feature vectors containing common characteristics at the pixel level or regionally [18; 19]. Image fusion at the data level necessitates stringent requirements regarding the resolution, pixel alignment, image quality, positional displacement, and additional aspects of the constituent images. Failure to satisfy these prerequisites results in substantial fusion errors. Figure 1c demonstrates image fusion using PCNN (Pulse Coupled Neural Network) [20] for feature extraction followed by fusion, while Figure 1d shows fusion based on SIFT (Scale Invariant Feature Transform) [21] feature point extraction. While these techniques can effectively fuse and denoise infrared and visible images, they degrade image quality for scenes containing small targets. Specifically, the fused result fails to enhance the feature information of the small targets and actually reduces discernible information about them. The fused image not only does not enhance the feature information of small targets but also reduces the information of small targets. The target detection method by [PERSON] et al. [22] uses convolutional neural networks to detect targets in infrared and visible light images, respectively, and combines the detection results through weighted fusion to improve detection performance. However, this method only fuses the detection results and lacks complementary feature information. [PERSON] et al. [23] also proposed a decision-level fusion algorithm for target detection of visible and infrared images. Although a model-based reliable fusion strategy was used instead of the weighted fusion strategy proposed by [PERSON] detection effect was improved. However, the essence was still to fuse the detection results of the two models, lacking the extraction and complementarity of target feature information. [PERSON] et al. (2019) used fully convolutional neural networks to extract features from infrared and visible light images, which were then weighted. They fused the features by calculating weights under different modalities, enriching the target feature information. However, this weighting method enriches the features and introduces more noise. This paper aims to review existing research results and effectively utilize target information in infrared and visible light images to improve the accuracy and robustness of small-scale FOD detection tasks in complex environments. A dual-mode small-scale FOD detection algorithm, which integrates multiple attention mechanisms, was proposed for this purpose. By improving the input layer of the model, the original infrared image and visible light image are input into the model at the same time so that the network can extract more and more original infrared and visible light feature information. Reduced noise generation and enriched feature extraction information compared to previous studies. And a large number of comparative experiments were conducted on collected datasets to demonstrate the superiority of the proposed method.