Joint discriminative representation learning for end-to-end person search

Zhang, Pengcheng; Yu, Xiaohan; Bai, Xiao; Wang, Chen; Zheng, Jin; Ning, Xin

doi:10.1016/j.patcog.2023.110053

Cited by 18 publications

(2 citation statements)

References 36 publications

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“…In recent years, deep learning technology has made significant progress in solving the collaborative operation and coordinated control problems of logistics and warehousing robots. Deep learning models, such as convolutional neural networks (CNN), reinforcement learning algorithms, and time series prediction methods, have been widely used to improve robots' perception, decision-making, and control capabilities (Zhang et al, 2023 ). Researchers have begun to explore how to use deep learning to optimize the operation of logistics warehousing robot swarms to achieve more efficient logistics management.…”

Section: Introductionmentioning

confidence: 99%

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Wang,

Liu

2024

Front. Neurorobot.

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IntroductionIn the field of logistics warehousing robots, collaborative operation and coordinated control have always been challenging issues. Although deep learning and reinforcement learning methods have made some progress in solving these problems, however, current research still has shortcomings. In particular, research on adaptive sensing and real-time decision-making of multi-robot swarms has not yet received sufficient attention.MethodsTo fill this research gap, we propose a YOLOv5-PPO model based on A3C optimization. This model cleverly combines the target detection capabilities of YOLOv5 and the PPO reinforcement learning algorithm, aiming to improve the efficiency and accuracy of collaborative operations among logistics and warehousing robot groups.ResultsThrough extensive experimental evaluation on multiple datasets and tasks, the results show that in different scenarios, our model can successfully achieve multi-robot collaborative operation, significantly improve task completion efficiency, and maintain target detection and environment High accuracy of understanding.DiscussionIn addition, our model shows excellent robustness and adaptability and can adapt to dynamic changes in the environment and fluctuations in demand, providing an effective method to solve the collaborative operation problem of logistics warehousing robots.

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Section: Introductionmentioning

confidence: 99%

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Wang,

Liu

2024

Front. Neurorobot.

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“…This means that the CNN shows great potential in image analysis tasks in the field of mining robot maintenance, especially in identifying tiny cracks, corrosion or other forms of defects on the surfaces of equipment [7]. However, in the face of analysis tasks involving complex multi-modal time series data, such as processing images, sound and sensor data simultaneously, the CNN needs to integrate additional structures for effective information fusion [8].…”

Section: Introductionmentioning

confidence: 99%

Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning

Li,

Fei

2024

Electronics

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In the field of mining robot maintenance, in order to enhance the research on predictive modeling, we introduce the LODS model (long short-term memory network (LSTM) optimized deep fusion neural network (DFNN) with spatiotemporal attention network (STAN)). Traditional models have shortcomings in handling the long-term dependencies of time series data and mining the complexity of spatiotemporal information in the field of mine maintenance. The LODS model integrates the advantages of LSTM, DFNN and STAN, providing a comprehensive method for effective feature extraction and prediction. Through experimental evaluation on multiple data sets, the experimental results show that the LODS model achieves more accurate predictions, compared with traditional models and optimization strategies, and achieves significant reductions in MAE, MAPE, RMSE and MSE of 15.76, 5.59, 2.02 and 11.96, respectively, as well as significant reductions in the number of parameters and computational complexity. It also achieves higher efficiency in terms of the inference time and training time. The LODS model performs well in all the evaluation indexes and has significant advantages; thus, it can provide reliable support for the equipment failure prediction of the mine maintenance robot.

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An end-to-end occluded person re-identification network with smoothing corrupted feature prediction

Zhao,

Qin,

Zhang

et al. 2024

Artif Intell Rev

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Occluded person re-identification (ReID) is a challenging task as the images suffer from various obstacles and less discriminative information caused by incomplete body parts. Most current works rely on auxiliary models to infer the visible body parts and partial-level features matching to overcome the contaminated body information, which consumes extra inference time and fails when facing complex occlusions. More recently, some methods utilized masks provided from image occlusion augmentation (OA) for the supervision of mask learning. These works estimated occlusion scores for each part of the image by roughly dividing it in the horizontal direction, but cannot accurately predict the occlusion, as well as failing in vertical occlusions. To address this issue, we proposed a Smoothing Corrupted Feature Prediction (SCFP) network in an end-to-end manner for occluded person ReID. Specifically, aided by OA that simulates occlusions appearing in pedestrians and providing occlusion masks, the proposed Occlusion Decoder and Estimator (ODE) estimates and eliminates corrupted features, which is supervised by mask labels generated via restricting all occlusions into a group of patterns. We also designed an Occlusion Pattern Smoothing (OPS) to improve the performance of ODE when predicting irregular obstacles. Subsequently, a Local-to-Body (L2B) representation is constructed to mitigate the limitation of the partial body information for final matching. To investigate the performance of SCFP, we compared our model to the existing state-of-the-art methods in occluded and holistic person ReID benchmarks and proved that our method achieves superior results over the state-of-the-art methods. We also achieved the highest Rank-1 accuracies of 70.9%, 87.0%, and 93.2% in Occluded-Duke, Occluded-ReID, and P-DukeMTMC, respectively. Furthermore, the proposed SCFP generalizes well in holistic datasets, yielding accuracies of 95.8% in Market-1510 and 90.7% in DukeMTMC-reID.

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Joint discriminative representation learning for end-to-end person search

Cited by 18 publications

References 36 publications

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Research on multi-robot collaborative operation in logistics and warehousing using A3C optimized YOLOv5-PPO model

Construction of Mining Robot Equipment Fault Prediction Model Based on Deep Learning

An end-to-end occluded person re-identification network with smoothing corrupted feature prediction

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