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学者姓名：陈锋
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随着远程监控和人工智能的融合发展,传统的码率优化算法并不适用于现阶段的移动监控网络场景.在机器视觉应用场景中,相对于传统码率优化算法只关注视频的质量,机器更关注于视频所表达的语义信息.以5G路侧摄像头远程智能检测为应用场景,提出一种基于视频语义的码率优化算法,在有限的码率传输范围内最大化目标检测准确率.具体地,该算法引入视频语义任务模型,将目标检测作为语义任务.分析目标比特与语义之间的特征关系,建立复杂度与运动区域结合的新权重来分配目标比特,使目标检测准确率达到最大化.实验结果表明,相较于HM16.23所使用的帧级树编码单元(Coding Tree Unit,CTU)层码率控制算法,所提算法不仅能够节省码率而且更符合无线远程监控的目标检测需求.在测试环境下平均提升了 1.4％的目标检测准确率,最高能够提升2.5％的目标检测准确率.
Keyword ：
人工智能 人工智能 机器视觉 机器视觉 目标检测 目标检测 视频语义 视频语义
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| GB/T 7714 | 黄发仁 , 柯捷铭 , 郑楚飞 et al. 基于视频语义的码率控制算法 [J]. | 无线电工程 , 2024 , 54 (8) : 1890-1899 . |
| MLA | 黄发仁 et al. "基于视频语义的码率控制算法" . | 无线电工程 54 . 8 (2024) : 1890-1899 . |
| APA | 黄发仁 , 柯捷铭 , 郑楚飞 , 周简心 , 张森林 , 陈锋 . 基于视频语义的码率控制算法 . | 无线电工程 , 2024 , 54 (8) , 1890-1899 . |
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随着智能多媒体技术和人工智能技术的融合发展，目标检测已广泛应用于移动监控网络远程传输场景。为了提升视频的目标检测精度，提出一种针对目标检测的码率优化算法，在有限的码率范围内使得物体的检测准确率达到最大化。将目标检测作为视频语义任务，分析视频码率比特与目标检测之间的关系，建立目标比特分配模型，从而使目标检测准确率达到最大化。实验结果表明，所提算法不仅能节省视频码率，还能提升目标检测精度。
Keyword ：
人工智能 人工智能 目标检测 目标检测 视频语义 视频语义
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| GB/T 7714 | 黄发仁 , 陈锋 , 吴宜婷 et al. 基于目标检测的码率优化算法 [J]. | 电视技术 , 2024 , 48 (04) : 20-24 . |
| MLA | 黄发仁 et al. "基于目标检测的码率优化算法" . | 电视技术 48 . 04 (2024) : 20-24 . |
| APA | 黄发仁 , 陈锋 , 吴宜婷 , 林灿辉 . 基于目标检测的码率优化算法 . | 电视技术 , 2024 , 48 (04) , 20-24 . |
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Anomaly detection is represented as an unsupervised learning to identify deviated images from normal images. In general, there are two main challenges of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of anomalies. In this paper, we propose a multiresolution feature guidance method based on Transformer named GTrans for unsupervised anomaly detection and localization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on ImageNet is developed to provide surrogate labels for features and tokens. Under the tacit knowledge guidance of the AGN, the anomaly detection network named Trans utilizes Transformer to effectively establish a relationship between features with multiresolution, enhancing the ability of the Trans in fitting the normal data manifold. Due to the strong generalization ability of AGN, GTrans locates anomalies by comparing the differences in spatial distance and direction of multi-scale features extracted from the AGN and the Trans. Our experiments demonstrate that the proposed GTrans achieves state-of-the-art performance in both detection and localization on the MVTec AD dataset. GTrans achieves image-level and pixel-level anomaly detection AUROC scores of 99.0% and 97.9% on the MVTec AD dataset, respectively.
Keyword ：
Anomaly detection Anomaly detection Deep learning Deep learning Knowledge distillation Knowledge distillation Transformer Transformer
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| GB/T 7714 | Yan, Shuting , Chen, Pingping , Chen, Honghui et al. Multiresolution feature guidance based transformer for anomaly detection [J]. | APPLIED INTELLIGENCE , 2024 , 54 (2) : 1831-1846 . |
| MLA | Yan, Shuting et al. "Multiresolution feature guidance based transformer for anomaly detection" . | APPLIED INTELLIGENCE 54 . 2 (2024) : 1831-1846 . |
| APA | Yan, Shuting , Chen, Pingping , Chen, Honghui , Mao, Huan , Chen, Feng , Lin, Zhijian . Multiresolution feature guidance based transformer for anomaly detection . | APPLIED INTELLIGENCE , 2024 , 54 (2) , 1831-1846 . |
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In autonomous driving, the fusion of multiple sensors is considered essential to improve the accuracy and safety of 3D object detection. Currently, a fusion scheme combining low-cost cameras with highly robust radars can counteract the performance degradation caused by harsh environments. In this paper, we propose the IRBEVF-Q model, which mainly consists of BEV (Bird’s Eye View) fusion coding module and an object decoder module.The BEV fusion coding module solves the problem of unified representation of different modal information by fusing the image and radar features through 3D spatial reference points as a medium. The query in the object decoder, as a core component, plays an important role in detection. In this paper, Heat Map-Guided Query Initialization (HGQI) and Dynamic Position Encoding (DPE) are proposed in query construction to increase the a priori information of the query. The Auxiliary Noise Query (ANQ) then helps to stabilize the matching. The experimental results demonstrate that the proposed fusion model IRBEVF-Q achieves an NDS of 0.575 and a mAP of 0.476 on the nuScenes test set. Compared to recent state-of-the-art methods, our model shows significant advantages, thus indicating that our approach contributes to improving detection accuracy. © 2024 by the authors.
Keyword ：
3D object detection 3D object detection attention mechanism attention mechanism multimodal fusion multimodal fusion query optimization query optimization transformer transformer
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| GB/T 7714 | Cai, G. , Chen, F. , Guo, E. . IRBEVF-Q: Optimization of Image–Radar Fusion Algorithm Based on Bird’s Eye View Features [J]. | Sensors , 2024 , 24 (14) . |
| MLA | Cai, G. et al. "IRBEVF-Q: Optimization of Image–Radar Fusion Algorithm Based on Bird’s Eye View Features" . | Sensors 24 . 14 (2024) . |
| APA | Cai, G. , Chen, F. , Guo, E. . IRBEVF-Q: Optimization of Image–Radar Fusion Algorithm Based on Bird’s Eye View Features . | Sensors , 2024 , 24 (14) . |
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Detection-based tracking is one of the main methods of multi-object tracking. It can achieve good tracking performance when using excellent detectors but it may associate wrong targets when facing overlapping and low-confidence detections. To address this issue, this article proposes a novel multi-object tracker (SCTracker) by exploiting shape constraint and confidence. In the data association stage, an intersection of union (IoU) distance with shape constraints is developed to calculate the cost matrix between tracks and detections, which can reduce the track of the wrong target with the similar position but inconsistent shape. Moreover, the detection confidence is calculated in the update stage of the Kalman filter to improve the track performance with the inaccurate detection result. Experimental results on the MOT 17 dataset show that the proposed SCTracker can improve the tracking performance of multi-object tracking when compared with the state-of-the-art methods. © 2001-2012 IEEE.
Keyword ：
Deep learning Deep learning Feature extraction Feature extraction Kalman filters Kalman filters Motion estimation Motion estimation Radar tracking Radar tracking Target tracking Target tracking Tracking radar Tracking radar
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| GB/T 7714 | Mao, Huan , Chen, Yulin , Li, Zongtan et al. SCTracker: Multi-Object Tracking With Shape and Confidence Constraints [J]. | IEEE Sensors Journal , 2024 , 24 (3) : 3123-3130 . |
| MLA | Mao, Huan et al. "SCTracker: Multi-Object Tracking With Shape and Confidence Constraints" . | IEEE Sensors Journal 24 . 3 (2024) : 3123-3130 . |
| APA | Mao, Huan , Chen, Yulin , Li, Zongtan , Chen, Pingping , Chen, Feng . SCTracker: Multi-Object Tracking With Shape and Confidence Constraints . | IEEE Sensors Journal , 2024 , 24 (3) , 3123-3130 . |
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本发明涉及一种基于时间戳和丢包检测的实时视频传输动态延时控制系统，包括发送端和接收端；所述发送端包括相连接的音视频编码单元和发送缓存单元；所述接收端包括接收缓存单元、视频重组单元、延时处理单元和音视频解码单元；所述接收缓存单元与视频重组单元、延时处理单元分别连接；所述视频重组单元还与音视频解码单元连接；所述发送端和接收端通过链路连接；所述延时处理单元采用平滑延时策略。本发明可以有效避免网络的进一步恶化和增加网络资源的利用率，提前实时动态调整数据包的缓冲时间，节约了有限的网络资源，提升了视频传输的高可靠性和用户的体验感。
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| GB/T 7714 | 陈锋 , 蔡吉玲 , 王君 et al. 基于时间戳和丢包检测的实时视频传输动态延时控制系统 : CN202210503856.5[P]. | 2022-05-10 00:00:00 . |
| MLA | 陈锋 et al. "基于时间戳和丢包检测的实时视频传输动态延时控制系统" : CN202210503856.5. | 2022-05-10 00:00:00 . |
| APA | 陈锋 , 蔡吉玲 , 王君 , 黄发仁 . 基于时间戳和丢包检测的实时视频传输动态延时控制系统 : CN202210503856.5. | 2022-05-10 00:00:00 . |
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本发明提出一种基于激光雷达的点云实时采集压缩传输系统及方法，包括：实时采集激光雷达点云，对点云进行自适应编码和封装，实时传输，解封装和自适应解码，渲染可视化并保存本地。本系统具有时间复杂度低，实时性高的优点，根据带宽动态压缩后的数据在低带宽的情况下也可实现可靠低时延的传输，远程实时地观测并处理激光雷达采集的第一手3D点云数据。高带宽情况下该系统还可用于传输多路数据，符合车路协同、远程智能驾驶、机器人视觉等行业对远程采集传输点云数据并进行分析处理的低时延需求。
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| GB/T 7714 | 陈建 , 黄炜 , 陈锋 et al. 基于激光雷达的点云实时采集压缩传输系统及方法 : CN202111074168.3[P]. | 2021-09-14 00:00:00 . |
| MLA | 陈建 et al. "基于激光雷达的点云实时采集压缩传输系统及方法" : CN202111074168.3. | 2021-09-14 00:00:00 . |
| APA | 陈建 , 黄炜 , 陈锋 , 郑明魁 , 黄昕 . 基于激光雷达的点云实时采集压缩传输系统及方法 : CN202111074168.3. | 2021-09-14 00:00:00 . |
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本发明提出一种基于跨层的多维参数的5G网络带宽预测系统及方法，包括：发送端、预测模型以及接收端；所述发送端采集不同基站服务区的流量数据，进行分类，并选取一组数据发送至预测模型；所述预测模型训练神经网络进行误差梯度下降时，反馈最新的误差数据给发送端；发送端进行精度分析以确定模型的预测效果和当前的物理层信息，随后对所发数据进行校正；所述接收端共有两个数据缓冲区，一个用于存储发送端发送给预测模型的实际流量数据，另一个用于存储预测模型所预测的数据；计算两者的MSE大小，再通过查阅奖励值r表，根据MES的大小给出相应的r值，与新预测状态一同反馈给预测网络的神经网络训练部分。能减少因位置变化造成突发流量而失准。
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| GB/T 7714 | 陈锋 , 毛豪滨 , 陈平平 . 基于跨层的多维参数的5G网络带宽预测系统及方法 : CN202111337402.7[P]. | 2021-11-12 00:00:00 . |
| MLA | 陈锋 et al. "基于跨层的多维参数的5G网络带宽预测系统及方法" : CN202111337402.7. | 2021-11-12 00:00:00 . |
| APA | 陈锋 , 毛豪滨 , 陈平平 . 基于跨层的多维参数的5G网络带宽预测系统及方法 : CN202111337402.7. | 2021-11-12 00:00:00 . |
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本发明涉及一种物理层网络编码下映射设计方法。通过该方法可以获得高性能的映射方案，可被用于联合物理层网络编码和卷积码的系统中。在两个终端节点A、B通过中继节点C进行信息交换时，两个终端同时将信息发送给中继节点C，C同时接收到两个终端的消息，并对这个叠加了两组数据进行解码处理得到新的信息，最后在第二个时隙将这个叠加的消息广播给两个终端节点A、B，中继节点各自从接收的消息出译码出对方终端的信息，从而提高网络的吞吐量。
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| GB/T 7714 | 陈平平 , 许方锦 , 林志坚 et al. 一种物理层网络编码下映射设计方法 : CN202111628793.8[P]. | 2021-12-28 00:00:00 . |
| MLA | 陈平平 et al. "一种物理层网络编码下映射设计方法" : CN202111628793.8. | 2021-12-28 00:00:00 . |
| APA | 陈平平 , 许方锦 , 林志坚 , 陈锋 . 一种物理层网络编码下映射设计方法 : CN202111628793.8. | 2021-12-28 00:00:00 . |
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本实用新型公开了一种具有防水结构的扬声器组件，包括扬声器本体以及卡设于扬声器本体内的防水组件，扬声器本体内具有音响，而防水组件设有相对应的防水斗，并且防水斗套设在音响外；防水组件的边缘通过橡胶条与扬声器本体的内侧面呈过盈配合，并且防水斗朝内侧上部倾斜延伸，由于防水斗内端表面具有一向外凸起的弯折角，并且该弯折角错开防水斗小口径一侧的下端边缘设置，当液体顺着扬声器本体的表面滑落至防水斗内时，能够通过该弯折角的设置让液体不再持续随着防水斗的内侧面流动，且让液体滴落并顺着防水斗的表面向外排出，并且可利用防水组件外侧的橡胶条来提高固定性，并且避免液体的渗入。
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| GB/T 7714 | 陈锋 , 林炜 . 一种具有防水结构的扬声器组件 : CN202321330561.9[P]. | 2023-05-30 00:00:00 . |
| MLA | 陈锋 et al. "一种具有防水结构的扬声器组件" : CN202321330561.9. | 2023-05-30 00:00:00 . |
| APA | 陈锋 , 林炜 . 一种具有防水结构的扬声器组件 : CN202321330561.9. | 2023-05-30 00:00:00 . |
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