DG2GAN: improving defect recognition performance with generated defect image sample.

This article aims to improve the deep-learning-based surface defect recognition. In actual manufacturing processes, there are issues such as data imbalance, insufficient diversity, and poor quality of augmented data in the collected image data for product defect recognition. A novel defect generation method with multiple loss functions, DG2GAN is presented in this paper. This method employs cycle consistency loss to generate defect images from a large number of defect-free images, overcoming the issue of imbalanced original training data. DJS optimized discriminator loss is introduced in the added discriminator to encourage the generation of diverse defect images. Furthermore, to maintain diversity in generated images while improving image quality, a new DG2 adversarial loss is proposed with the aim of generating high-quality and diverse images. The experiments demonstrated that DG2GAN produces defect images of higher quality and greater diversity compared with other advanced generation methods. Using the DG2GAN method to augment defect data in the CrackForest and MVTec datasets, the defect recognition accuracy increased from 86.9 to 94.6%, and the precision improved from 59.8 to 80.2%. The experimental results show that using the proposed defect generation method can obtain sample images with high quality and diversity and employ this method for data augmentation significantly enhances surface defect recognition technology.

Real-time citrus variety detection in orchards based on complex scenarios of improved YOLOv7.

Variety detection provides technical support for selecting XinHui citrus for use in the production of XinHui dried tangerine peel. Simultaneously, the mutual occlusion between tree leaves and fruits is one of the challenges in object detection. In order to improve screening efficiency, this paper introduces a YOLO(You Only Look Once)v7-BiGS(BiFormer&GSConv) citrus variety detection method capable of identifying different citrus varieties efficiently. In the YOLOv7-BiGS network model, initially, the BiFormer attention mechanism in the backbone of the YOLOv7-based network strengthens the model's ability to extract citrus' features. In addition, the introduction of the lightweight GSConv convolution in place of the original convolution within the ELAN of the head component effectively streamlines model complexity while maintaining performance integrity. To environment challenge validate the effectiveness of the method, the proposed YOLOv7-BiGS was compared with YOLOv5, YOLOv7, and YOLOv8. In the comparison of YOLOv7-BiGS with YOLOv5, YOLOv7, and YOLOv8, the experimental results show that the precision, mAP and recell of YOLOv7-BiGS are 91%, 93.7% and 87.3% respectively. Notably, compared to baseline methods, the proposed approach exhibited significant enhancements in precision, mAP, and recall by 5.8%, 4.8%, and 5.2%, respectively. To evaluate the efficacy of the YOLOv7-BiGS in addressing challenges posed by complex environmental conditions, we collected occluded images of Xinhui citrus fruits from the Xinhui orchard base for model detection. This research aims to fulfill performance criteria for citrus variety identification, offering vital technical backing for variety detection endeavors.

Immobilized Ag3PO4/GO on 3D nickel foam and its photocatalytic degradation of norfloxacin antibiotic under visible light.

In this study, a series of Ag3PO4/graphene oxide (GO) films were dip-coated on a metal nickel foam. The immobilized catalysts were characterized by X-ray diffraction, scanning electron microscopy, X-ray photoelectron spectroscopy, ultraviolet-visible spectroscopy, Raman spectroscopy, high-resolution transmission electron microscopy and photoluminescence spectroscopy. The results show that Ag3PO4/GO was successfully supported on a nickel foam. The photocatalytic activity of the film catalyst under visible light was investigated by the degradation of norfloxacin, an antibiotic. Photocatalytic stability of this catalyst was also investigated. An optimized film exhibited superior activity and stability, the degradation rate of norfloxacin was about 83.68% in 100 min and the reaction rate constant k was 1.9 times that of pristine Ag3PO4. Further investigation found that photo-generated holes (h+) and superoxide anion radicals (·O2 -) are the main active species in the photodegradation process. The result indicates that the addition of GO inhibits the recombination of photogenerated electron-hole pairs, and thus has improved the photocatalytic activity and cyclic stability under visible light. The photocatalytic mechanism of the film catalyst was proposed. The prepared Ag3PO4/GO film catalyst is a promising candidate for treatment of wastewater containing antibiotics.