# A Titanium Alloy Defect Detection Method Based on Optical-Acoustic Image Fusion
###### Abstract  
Nowadays, a single detection method is insufficient for comprehensively and clearly identifying both surface defects and inner defects in titanium alloys. To address this limitation, this paper proposes a titanium alloy defect detection method based on optical-acoustic image fusion. A detection system was developed to achieve comprehensive and precise inspection of titanium alloys by integrating advanced deep learning-based optical testing technology, reliable C-scan ultrasonic detection technology, and information fusion techniques. Furthermore, the PC software can output interactive fusion results and generate decision-level detection reports. The experimental results demonstrate that the surface defect detection algorithm achieves an accuracy of 99.0%, with a surface defect size measurement resolution of 0.01 mm, an internal defect size measurement resolution of 1 mm, and a positional error within 2 mm. It was found that the proposed method provides a potential solution for the practical application of inspecting surface defects and inner defects in the materials. nodestructive testing; titanium alloy; YOLO; ultrasonic; fusion Article  
12 June 20  
industry in the field of metal surface defect detection [3]. The target detection technology can effectively overcome the limitation of manual detection methods with the improvement of efficiency and accuracy in detection. In 2025, [PERSON] [4] proposed a lightweight and efficient real-time metal surface defect detection method, LDE-YOLO, based on YOLOv8. Compared with the traditional approach, this method had the advantages of high precision and efficiency. The accuracy of this model can reach 80.8% on NEU-DET. In 2024, [PERSON] [5] proposed a rail defect detection algorithm based on YOLO-FCA. The model was improved based on YOLOv7, which had the advantages of stability, high efficiency, and accuracy. In the rail defect detection experiment, the accuracy of the YOLO-FCA algorithm reached 80.7%, and the detection speed reached 212.5 FPS. It is hard to find internal metal defects via conventional appearance detection because of their concealment. NDT has developed rapidly in recent years [6]. Under the premise of not damaging the components to be tested or not affecting their performance, the internal or surface structural state of the components is checked by means of physical or chemical methods and advanced ultrasonic, ray, infrared, and electromagnetic, among others, instruments and equipment, and the location, size, and shape of the defects are given. In 2024, [PERSON] [7] proposed a detection method for laser ultrasonic microdefects based on multi-channel optical fiber interference sensing. Using laser ultrasonic nondestructive testing technology, high precision positioning, and accurate size measurements, circular hole defects with a radius of 2 mm inside the metal were realized. In 2024, [PERSON] [8] proposed an acoustic emission technology for detecting internal damage in metals. This technology combined the Fourier transform, adaptive filtering, and other techniques to detect internal defects in metals, preventing material failure effectively. With the development of science and technology, multi-information fusion methods have received extensive attention from experts and scholars. In 2016, [PERSON] [9] proposed a metal material defect detection method based on optical, photoacoustic, and ultrasonic three-mode signals, aiming at the shortcomings of the single-signal mode and the limited detection range of the existing metal material nondestructive testing technology, as well as realized the comprehensive structural health diagnosis of metal materials. In 2022, [PERSON] [10] designed a joint system using millimeter wave radar and infrared imaging sensors. The system fuses and tracks the subject's torso and face information and realizes human body temperature screening and contact tracking on the basis of protecting privacy.