Bamboo surface defect detection provides quality assurance for bamboo product manufacture in industrial scenarios, an integral part of the overall manufacturing process. Currently, bamboo defect inspection predominantly relies on manual operation, but manual inspection is very time-consuming as well as labor-intensive, and the quality of inspection is not guaranteed. In recent years, a few visual inspection systems based on traditional image processing have been deployed in some factories. However, traditional machine vision algorithms extract features in tedious steps and have poor performance along with poor adaptability in the face of complex defects. Accordingly, many scholars are committed to seeking deep learning methods to accomplish surface defect detection. However, existing deep learning object detectors struggle with specific industrial defects when directly applied to industrial defect detection, such as sliver defects, especially for ones with extreme aspect ratios. To this end, this paper proposes an improved model based on the advanced object detector YOLOV4-CSP, which introduces asymmetric convolution and attention mechanism. The introduction of asymmetric convolution enhances the feature extraction in the horizontal direction of the bamboo strip surface, improving the performance in detecting sliver defects. In addition, convolutional block attention module(CBAM), a hybrid attention module, which combines channel attention with spatial attention, is utilized to promote the representation ability of the model by increasing the weights of crucial channels and regions. The proposed model achieves outstanding performance in the general categories and excels in the hard-to-detect categories. Some enterprise's bamboo strip dataset experiments verify that the model can reach 96.74% mAP for the typical six surface defects. Meanwhile, we also observe significant improvements when extending our model to aluminum datasets with similar characteristics.
Surface defect detection is a vital process in industrial production and a significant research direction in computer vision. Although today’s deep learning defect detection methods based on computer vision can achieve high detection accuracy, they are mainly based on supervised learning. They require many defect samples to train the model, which is not compatible with the current situation that industrial defect sample is difficult to obtain and costly to label. So we propose a new unsupervised small sample defect detection model-ISU-GAN, which is based on the CycleGAN architecture. A skip connection, SE module, and Involution module are added to the Generator, enabling the feature extraction capability of the model to be significantly improved. Moreover, we propose an SSIM-based defect segmentation method that applies to GAN-based defect detection and can accurately extract defect contours without the need for redundant noise reduction post-processing. Experiments on the DAGM2007 dataset show that the unsupervised ISU-GAN can achieve higher detection accuracy and finer defect profiles with less than 1/3 of the unlabelled training data than the supervised model with the full training set. Relative to the supervised segmentation models UNet and ResUNet++ with more training samples, our model improves the detection accuracy by 2.84% and 0.41% respectively and the F1 score by 0.025 and 0.0012 respectively. In addition, the predicted profile obtained using our method is closer to the real profile than other models used for comparison.
OBJECTIVE: To investigate whether the HMGB1 DAMP signaling pathway is involved in resveratrol anti-oxygen glucose deprivation (OGD)-induced microglial inflammatory processes and explore its underlying mechanisms. METHODS: Cell viability was tested by MTT assay to determine the appropriate resveratrol and EX527 concentration and OGD time, and the cells were divided into four groups: Control, OGD+DMSO, OGD+RES and OGD+RES+EX527, ELISA. Rt-PCR and western blot were used to detect inflammatory factors and HMGB1 signaling pathway-related protein changes. WB and immunofluorescence were used to demonstrate the localization of HMGB1 in cells, the acetylation level of HMGB1 and the interaction between HMGB1 and Sirt1 were detected by CoIP. Different groups BV2 cells were co-cultured with primary mouse neurons, and the release of HMGB1 was observed and the level of LDH in the supernatant was detected. RESULTS: We determined that RES (100umol), EX527 (100umol) and OGD3h were the optimal treatment conditions. RES inhibited the increase of inflammatory mediators and HMGB1 signaling pathway-related proteins and reduce the increase of HMGB1 level in cell supernatant after OGD, and EX-527 reversed this effect; immunofluorescence indicated that RES can limit HMGB1 in cells, however, different from the change of HMGB1 in the culture medium, there was no significant difference in the mRNA level of HMGB1 in each group, suggesting that the increase of HMGB1 level in supernatant after hypoxia is mainly due to the increase of active secretion rather than synthesis. CoIP results showed that the binding of HMGB1 to deacetylase SIRT1 was decreased and the level of acetylation was decreased after OGD. RES could increase the interaction between HMGB1 and sirt1 and increase the acetylation level of HMGB1 but EX527 reduced this interaction. In the neuron co-culture system, the extracellular release of HMGB1 and LDH was increased in the supernatant of the OGD+DMSO group, while this change in the RES group was attenuated, and the OGD+RES+EX527 group restored the increase of LDH and HMGB1. CONCLUSIONS: The activation of downstream signaling pathway by active release of HMGB1 is partially involved in OGD induced BV2 inflammatory process. Resveratrol reduces inflammation by inhibiting HMGB1 release, a role associated with its ability to activate SIRT1-mediated acetylation of HMGB1.
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