To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight arc...To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.展开更多
目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的s...目的探讨甲基转移酶5(methyltransferase-like 5,METTL5)在三阴乳腺癌(triple-negative breast cancer,TNBC)中的作用和潜在机制。方法采用免疫组织化学方法和Western blot检测TNBC肿瘤组织和细胞系中METTL5的表达情况。用靶向METTL5的shRNA(shRNA-METTL5)转染TNBC细胞后,用CCK-8、集落形成、伤口愈合以及Transwell实验分别检测细胞增殖活性、迁移与侵袭,Western blot检测Wnt/β-catenin信号关键蛋白的表达。构建异种移植瘤模型,验证敲降METTL5对TNBC细胞在体内生长以及Wnt/β-catenin信号活性的影响。结果METTL5在TNBC肿瘤组织和细胞系中表达上调(P<0.01)。敲降METTL5可抑制TNBC细胞的增殖、迁移和侵袭并降低了Wnt/β-catenin信号分子β-catenin、细胞周期蛋白(Cyclin)D1、基质金属蛋白酶(MMP)-2和MMP-7的表达(均P<0.01)。体内实验显示,敲降METTL5减缓了移植瘤生长和Wnt/β-catenin信号活性。结论敲降METTL5能抑制TNBC细胞的增殖、迁移与侵袭,其作用可能与抑制Wnt/β-catenin信号通路有关。展开更多
基金funded by the General Project of Key Research and Develop-ment Plan of Shaanxi Province(No.2022NY-087).
文摘To address the challenges of high complexity,poor real-time performance,and low detection rates for small target vehicles in existing vehicle object detection algorithms,this paper proposes a real-time lightweight architecture based on You Only Look Once(YOLO)v5m.Firstly,a lightweight upsampling operator called Content-Aware Reassembly of Features(CARAFE)is introduced in the feature fusion layer of the network to maximize the extraction of deep-level features for small target vehicles,reducing the missed detection rate and false detection rate.Secondly,a new prediction layer for tiny targets is added,and the feature fusion network is redesigned to enhance the detection capability for small targets.Finally,this paper applies L1 regularization to train the improved network,followed by pruning and fine-tuning operations to remove redundant channels,reducing computational and parameter complexity and enhancing the detection efficiency of the network.Training is conducted on the VisDrone2019-DET dataset.The experimental results show that the proposed algorithmreduces parameters and computation by 63.8% and 65.8%,respectively.The average detection accuracy improves by 5.15%,and the detection speed reaches 47 images per second,satisfying real-time requirements.Compared with existing approaches,including YOLOv5m and classical vehicle detection algorithms,our method achieves higher accuracy and faster speed for real-time detection of small target vehicles in edge computing.
