As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex b...As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.展开更多
目的探讨小骨窗开颅手术治疗对高血压脑出血患者临床疗效及预后的影响。方法选取2018年1月~2021年1月本院收治高血压脑出血患者124例。根据治疗方案分为对照组(颅骨去骨瓣减压术)61例和试验组(小骨窗开颅手术)63例。观察两组临床疗效;...目的探讨小骨窗开颅手术治疗对高血压脑出血患者临床疗效及预后的影响。方法选取2018年1月~2021年1月本院收治高血压脑出血患者124例。根据治疗方案分为对照组(颅骨去骨瓣减压术)61例和试验组(小骨窗开颅手术)63例。观察两组临床疗效;通过日常生活能力评分(Activity of Daily Living Scale,ADL)及美国国立卫生院神经功能缺损(National Institutes of Health Stroke Scales,NIHSS)评分、格拉斯预后评分评估预后情况。结果试验组临床总有效率92.06%(58/63),对照组70.48%(43/61),差异有统计学意义(P<0.05);治疗后,试验组ADL评分和格拉斯预后评分高于对照组,NIHSS评分低于对照组(P<0.05)。结论小骨窗开颅手术治疗高血压脑出血患者,能提升生活能力及改善预后。展开更多
基金supported in part by the National Natural Science Foundation of China under Grants 32171909,51705365,52205254The Guangdong Basic and Applied Basic Research Foundation under Grants 2020B1515120050,2023A1515011255+2 种基金The Guangdong Key R&D projects under Grant 2020B0404030001the Scientific Research Projects of Universities in Guangdong Province under Grant 2020KCXTD015The Ji Hua Laboratory Open Project under Grant X220931UZ230.
文摘As computer technology continues to advance,factories have increasingly higher demands for detecting defects.However,detecting defects in a plant environment remains a challenging task due to the presence of complex backgrounds and defects of varying shapes and sizes.To address this issue,this paper proposes YOLO-DD,a defect detectionmodel based on YOLOv5 that is effective and robust.To improve the feature extraction process and better capture global information,the vanilla YOLOv5 is augmented with a new module called Relative-Distance-Aware Transformer(RDAT).Additionally,an Information Gap Filling Strategy(IGFS)is proposed to improve the fusion of features at different scales.The classic lightweight attention mechanism Squeeze-and-Excitation(SE)module is also incorporated into the neck section to enhance feature expression and improve the model’s performance.Experimental results on the NEU-DET dataset demonstrate that YOLO-DDachieves competitive results compared to state-of-the-art methods,with a 2.0% increase in accuracy compared to the original YOLOv5,achieving 82.41% accuracy and38.25FPS(framesper second).Themodel is also testedon a self-constructed fabric defect dataset,and the results show that YOLO-DD is more stable and has higher accuracy than the original YOLOv5,demonstrating its stability and generalization ability.The high efficiency of YOLO-DD enables it to meet the requirements of industrial high accuracy and real-time detection.
文摘目的探讨小骨窗开颅手术治疗对高血压脑出血患者临床疗效及预后的影响。方法选取2018年1月~2021年1月本院收治高血压脑出血患者124例。根据治疗方案分为对照组(颅骨去骨瓣减压术)61例和试验组(小骨窗开颅手术)63例。观察两组临床疗效;通过日常生活能力评分(Activity of Daily Living Scale,ADL)及美国国立卫生院神经功能缺损(National Institutes of Health Stroke Scales,NIHSS)评分、格拉斯预后评分评估预后情况。结果试验组临床总有效率92.06%(58/63),对照组70.48%(43/61),差异有统计学意义(P<0.05);治疗后,试验组ADL评分和格拉斯预后评分高于对照组,NIHSS评分低于对照组(P<0.05)。结论小骨窗开颅手术治疗高血压脑出血患者,能提升生活能力及改善预后。
