Light-emitting diode (LED) chips have disordered arrangement and defects with characteristics of low contrast, for which traditional segmentation methods cannot classify surface defects effectively. In this paper, a chip segmentation method based on position pre-estimation and a modified Normalized Correlation Coefficient (NCC) matching algorithm, as well as feature enhancement methods are proposed. The position pre-estimation method is used to avoid the interference introduced by the disordered chip arrangement and the large missing area. By modifying the NCC algorithm, matching speed is improved by eight times compared to traditional NCC while matching result is not affected by brightness change. Furthermore, feature enhancement schemes with higher speed and accuracy were designed to identify low-contrast defects. The experimental results showed that the average accuracy reached 99.54%, improved by 0.66% compared to the state-of-the-art method while the inspection missing rate was 0.03%. In addition, the detection time of a single chip was approximately 1.098 ms, which meets the requirements of online detection, and the smallest defect that could be detected was 2 µm. In summary, the methods proposed in this study meet the requirements of industrial online detection regardless of accuracy, efficiency, or extensibility.

Due to the intricate and diverse nature of textile defects, detecting them poses an exceptionally challenging task. In comparison with conventional defect detection methods, deep learning-based defect detection methods generally exhibit superior precision. However, utilizing deep learning for defect detection requires a substantial volume of training data, which can be particularly challenging to accumulate for textile flaws. To augment the fabric defect dataset and enhance fabric defect detection accuracy, we propose a fabric defect image generation method based on Pix2Pix generative adversarial network. This approach devises a novel dual-stage W-net generative adversarial network. By increasing the network depth, this model can effectively extract intricate textile image features, thereby enhancing its ability to expand information sharing capacity. The dual-stage W-net generative adversarial network allows generating desired defects on defect-free textile images. We conduct quality assessment of the generated fabric defect images resulting in peak signal-to-noise ratio and structural similarity values exceeding 30 and 0.930, respectively, and a learned perceptual image patch similarity value no greater than 0.085, demonstrating the effectiveness of fabric defect data augmentation. The effectiveness of dual-stage W-net generative adversarial network is established through multiple comparative experiments evaluating the generated images. By examining the detection performance before and after data augmentation, the results demonstrate that mean average precision improves by 6.13% and 14.57% on YOLO V5 and faster recurrent convolutional neural networks detection models, respectively.

Visualizing the dynamics of fine structures in cells requires noninvasive, long-duration imaging of the intracellular environment at high spatiotemporal resolution and low background noise. With modularized hardware and polarized laser modulation, we developed a nanoscale super-resolution structured illumination microscope (SR-SIM) imaging technique. Combined with a reliable image reconstruction algorithm and timing synchronization of all devices, the super-resolution (SR) images in hepatocytes reached 134.95 nm spatial resolution and 50 fps temporal resolution. This imaging system was able to maintain the optimal operation state over hundreds of time points due to less exposure and low phototoxicity. In hepatocytes, interactions between mitochondria and lipid droplets (LDs) underpin many crucial physiological processes, ranging from cellular metabolism to signaling. In this study, we pioneered the use of the SIM system for imaging the interaction characteristics between mitochondria and LDs. More than 200 hepatocytes were counted and recorded effectively. We found that LDs in an unstable state were divided under mitochondrial contact and fused without it. Among 200 LDs, more than 69% were surrounded by mitochondria that tended to wrap LDs. The SR-SIM imaging technique was demonstrated to break the limitations of conventional imaging methods in spatial-temporal resolution and imaging duration in the field of the dynamic study between mitochondria and LDs.