Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5

Aiming at the problems of low efficiency and poor accuracy in conventional surface defect detection methods for aero-engine components, a surface defect detection model based on an improved YOLOv5 object detection algorithm is proposed in this paper. First, a k-means clustering algorithm was used to recalculate the parameters of the preset anchors to make them match the samples better. Then, an ECA-Net attention mechanism was added at the end of the backbone network to make the model pay more attention to feature extraction from defect areas. Finally, the PANet structure of the neck network was improved through its replacement with BiFPN modules to fully integrate the features of all scales. The results showed that the mAP of the YOLOv5s-KEB model was 98.3%, which was 1.0% higher than the original YOLOv5s model, and the average inference time for a single image was 2.6 ms, which was 10.3% lower than the original model. Moreover, compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms, the YOLOv5s-KEB model has the highest accuracy and the smallest size, which make it very efficient and convenient for practical applications

Surface Defect Detection Model for Aero-Engine Components Based on Improved YOLOv5

Aiming at the problems of low efficiency and poor accuracy in conventional surface defect detection methods for aero-engine components, a surface defect detection model based on an improved YOLOv5 object detection algorithm is proposed in this paper. First, a k-means clustering algorithm was used to recalculate the parameters of the preset anchors to make them match the samples better. Then, an ECA-Net attention mechanism was added at the end of the backbone network to make the model pay more attention to feature extraction from defect areas. Finally, the PANet structure of the neck network was improved through its replacement with BiFPN modules to fully integrate the features of all scales. The results showed that the mAP of the YOLOv5s-KEB model was 98.3%, which was 1.0% higher than the original YOLOv5s model, and the average inference time for a single image was 2.6 ms, which was 10.3% lower than the original model. Moreover, compared with the Faster R-CNN, YOLOv3, YOLOv4 and YOLOv4-tiny object detection algorithms, the YOLOv5s-KEB model has the highest accuracy and the smallest size, which make it very efficient and convenient for practical applications.</jats:p

Wide-Range Continuously-Tunable Slow-Light Delay Line Based on Stimulated Brillouin Scattering

Through selectively controlling the stimulated Brillouin scattering in optical fibers with different lengths, a continuously tunable time-delay scheme enabling to work in a large range is proposed in this letter. This is realized by connecting a fixed long single-mode fiber (SMF) to one of the several selectable short SMFs that successively have an equal increment in length. These short-length fibers are, respectively, fixed to the different channels between two identical optical switches. Therefore, a wide-range and continuously tunable slow-light delay line can be constructed by changing the power of the pump beam, assisted by switching to different channels. In the experiment, a time delay from 0 to 201.29 ns is demonstrated for a five-channel configuration. A further large-range time delay can be expected if one adds the number of channels accordingly

Theoretical and Experimental Study on Nonintrusive Light Injection Via Cladding in Plastic Optical Fibers

Based on the fiber macrobending and the refractive index matching technologies, a novel scheme of nonintrusive light injection, namely, the light signal is injected into the fiber core from the fiber cladding without any destruction, is proposed in plastic optical fibers (POFs). Using the ray-tracing method, a 3-D theoretical model is established to characterize the performance of the light injection. The influences of the fiber bending radius, the light source placement, and the surrounding medium refractive index on the light injection efficiency are investigated and assessed. Meanwhile, correlative experiments have also been conducted to contrast theoretical simulations. The experiment results fit theoretical ones well. This nonintrusive light injection technology in POFs might have many potential applications such as the optical signal uploading and downloading, the optical coupling, and the local area networks

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Polarimetric dehazing methods can significantly enhance the quality of hazy images. However, current methods are not robust enough under different imaging conditions. In this paper, we propose a generalized polarimetric dehazing method based on low-pass filtering in the frequency domain. This method can accurately estimate the polarized state of the scattering light automatically without adjusting bias parameters. Experimental results show the effectiveness and robustness of our proposed method in different hazy weather and scattering underwater environments with different densities. Furthermore, computational efficiency is enhanced more than 70% compared to the polarimetric dehazing method we proposed previously.Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

A Mueller matrix measurement technique based on a division-of-aperture polarimetric camera

When an object is illuminated by an incoming light described by a Stokes vector, the outgoing light scattered, reflected or transmitted from the object is modulated and its polarization property can be expressed by another Stokes vector. The transformation relation between the incoming and the outgoing Stokes vectors is called the Mueller matrix. The Mueller matrix completely characterizes the optical properties of the light scattered or transmitted from the object, including the diattenuation, the retardance and the depolarization. So, how to measure the Mueller matrix efficiently and accurately becomes considerably significant for its practical applications. We propose a new method for Mueller matrix fast acquisition based on a division-of-aperture simultaneous polarimetric imaging technique. Traditional methods for obtaining the 16 elements of the Mueller matrix require at least 16 polarimetric measurements. While in our method it is enough by just changing the states of polarization (SOPs) of the input light 4 times. These time-saving and easy calculating features are contributed to our specific polarimetric camera, where a full-Stokes vector is obtained easily since 3 linear SOPs (0&deg;, 45&deg;, 90&deg;) and 1 circular SOP can be recorded simultaneously by sharing the same detector. To simply verify the effectiveness of our method, polarizers (45&deg;, 90&deg;), and quarter-wave plates (0&deg;, 45&deg;) are chosen as samples to be measured. Experimental results show that they are consistent with the theoretical results, both in the Mueller matrix and the corresponding images. We predict that this method for Mueller matrix rapid acquisition can get wide potential applications. &copy; 2019 SPIE.</p

Generalized Polarimetric Dehazing Method Based on Low-Pass Filtering in Frequency Domain

Polarimetric dehazing methods can significantly enhance the quality of hazy images. However, current methods are not robust enough under different imaging conditions. In this paper, we propose a generalized polarimetric dehazing method based on low-pass filtering in the frequency domain. This method can accurately estimate the polarized state of the scattering light automatically without adjusting bias parameters. Experimental results show the effectiveness and robustness of our proposed method in different hazy weather and scattering underwater environments with different densities. Furthermore, computational efficiency is enhanced more than 70% compared to the polarimetric dehazing method we proposed previously.</jats:p

Dynamically Optimized Object Detection Algorithms for Aviation Safety

Infrared imaging technology demonstrates significant advantages in aviation safety monitoring due to its exceptional all-weather operational capability and anti-interference characteristics, particularly in scenarios requiring real-time detection of aerial objects such as airport airspace management. However, traditional infrared target detection algorithms face critical challenges in complex sky backgrounds, including low signal-to-noise ratio (SNR), small target dimensions, and strong background clutter, leading to insufficient detection accuracy and reliability. To address these issues, this paper proposes the AFK-YOLO model based on the YOLO11 framework: it integrates an ADown downsampling module, which utilizes a dual-branch strategy combining average pooling and max pooling to effectively minimize feature information loss during spatial resolution reduction; introduces the KernelWarehouse dynamic convolution approach, which adopts kernel partitioning and a contrastive attention-based cross-layer shared kernel repository to address the challenge of linear parameter growth in conventional dynamic convolution methods; and establishes a feature decoupling pyramid network (FDPN) that replaces static feature pyramids with a dynamic multi-scale fusion architecture, utilizing parallel multi-granularity convolutions and an EMA attention mechanism to achieve adaptive feature enhancement. Experiments demonstrate that the AFK-YOLO model achieves 78.6% mAP on a self-constructed aerial infrared dataset&mdash;a 2.4 percentage point improvement over the baseline YOLO11&mdash;while meeting real-time requirements for aviation safety monitoring (416.7 FPS), reducing parameters by 6.9%, and compressing weight size by 21.8%. The results demonstrate the effectiveness of dynamic optimization methods in improving the accuracy and robustness of infrared target detection under complex aerial environments, thereby providing reliable technical support for the prevention of mid-air collisions