130 research outputs found
PP-YOLOE-R: An Efficient Anchor-Free Rotated Object Detector
Arbitrary-oriented object detection is a fundamental task in visual scenes
involving aerial images and scene text. In this report, we present PP-YOLOE-R,
an efficient anchor-free rotated object detector based on PP-YOLOE. We
introduce a bag of useful tricks in PP-YOLOE-R to improve detection precision
with marginal extra parameters and computational cost. As a result,
PP-YOLOE-R-l and PP-YOLOE-R-x achieve 78.14 and 78.28 mAP respectively on DOTA
1.0 dataset with single-scale training and testing, which outperform almost all
other rotated object detectors. With multi-scale training and testing,
PP-YOLOE-R-l and PP-YOLOE-R-x further improve the detection precision to 80.02
and 80.73 mAP. In this case, PP-YOLOE-R-x surpasses all anchor-free methods and
demonstrates competitive performance to state-of-the-art anchor-based two-stage
models. Further, PP-YOLOE-R is deployment friendly and PP-YOLOE-R-s/m/l/x can
reach 69.8/55.1/48.3/37.1 FPS respectively on RTX 2080 Ti with TensorRT and
FP16-precision. Source code and pre-trained models are available at
https://github.com/PaddlePaddle/PaddleDetection, which is powered by
https://github.com/PaddlePaddle/Paddle.Comment: 6 pages, 2 figures, 3 table
Investigation of Bicycle Travel Time Estimation Using Bluetooth Sensors for Low Sampling Rates
Filtering the data for bicycle travel time using Bluetooth sensors is crucial to the estimation of link travel times on a corridor. The current paper describes an adaptive filtering algorithm for estimating bicycle travel times using Bluetooth data, with consideration of low sampling rates. The data for bicycle travel time using Bluetooth sensors has two characteristics. First, the bicycle flow contains stable and unstable conditions. Second, the collected data have low sampling rates (less than 1%). To avoid erroneous inference, filters are introduced to “purify” multiple time series. The valid data are identified within a dynamically varying validity window with the use of a robust data-filtering procedure. The size of the validity window varies based on the number of preceding sampling intervals without a Bluetooth record. Applications of the proposed algorithm to the dataset from Genshan East Road and Moganshan Road in Hangzhou demonstrate its ability to track typical variations in bicycle travel time efficiently, while suppressing high frequency noise signals.</p
Research on the Multiroute Probit-Based Public Transit Assignment Model Based on Bus Stop
A public transit network differs from a general road network. The passenger flow of bus stops and the limited capacity of buses have a greater effect than road traffic flow on the running time of buses. As a result, conventional public transit assignment models that adopt the econometric road network path concept have numerous limitations. Based on the analysis, the generalized bus trip time chain is analyzed, and the concept of a congestion function is proposed to describe the relationship between trip resistance and flow in the current paper. On the premise of this study, the transit network resistance function is formed and the multiroute probit-based loading model is established. With using STOCH or Dial's algorithm, the process of distribution is proposed. Finally, the model is applied to the transit network assignment of Deqing Town in Zhejiang Province. The result indicates that the model can be applied to practical operations with high-precision results
Offset Optimization Based on Queue Length Constraint for Saturated Arterial Intersections
Offset optimization is of critical importance to the traffic control system, especially when spillovers appear. In order to avoid vehicle queue spillovers, an arterial offset optimization model was presented in saturated arterial intersections based on minimizing the queue length over the whole duration of the saturated traffic environment. The paper uses the shockwave theory to analyze the queue evolution process of the intersection approach under the saturated traffic environment. Then through establishing and analyzing a function relationship between offset and the maximum queue length per cycle, a mapping model of offset and maximum queue length was established in the saturated condition. The validity and sensitivity of this model were tested by the VISSIM simulation environment. Finally, results showed that when volumes ratios are 0.525–0.6, adjusting offset reasonably under the saturated condition could decrease the queue length and effectively improve the vehicle operating efficiency
Large-scale Knowledge Distillation with Elastic Heterogeneous Computing Resources
Although more layers and more parameters generally improve the accuracy of
the models, such big models generally have high computational complexity and
require big memory, which exceed the capacity of small devices for inference
and incurs long training time. In addition, it is difficult to afford long
training time and inference time of big models even in high performance
servers, as well. As an efficient approach to compress a large deep model (a
teacher model) to a compact model (a student model), knowledge distillation
emerges as a promising approach to deal with the big models. Existing knowledge
distillation methods cannot exploit the elastic available computing resources
and correspond to low efficiency. In this paper, we propose an Elastic Deep
Learning framework for knowledge Distillation, i.e., EDL-Dist. The advantages
of EDL-Dist are three-fold. First, the inference and the training process is
separated. Second, elastic available computing resources can be utilized to
improve the efficiency. Third, fault-tolerance of the training and inference
processes is supported. We take extensive experimentation to show that the
throughput of EDL-Dist is up to 3.125 times faster than the baseline method
(online knowledge distillation) while the accuracy is similar or higher.Comment: To appear in Concurrency and Computation: Practice and Experience, 16
pages, 7 figures, 5 table
Removal efficacy of fly ash composite filler on tailwater nitrogen and phosphorus and its application in constructed wetlands
Constructed wetlands (CWs) have been widely used in tailwater treatment. However, it is difficult to achieve considerable removal efficiency of nitrogen and phosphorus in tailwater solely by CWs—an efficient green wetland filler is also important. This study investigated 160 domestic sewage treatment facilities (DSTFs) in rural areas from two urban areas in Jiaxing for TP and NH3-N and found that TP and NH3-N concentrations in rural domestic sewage (RDS) in this plain river network are still high. Therefore, we selected a new synthetic filler (FA-SFe) to enhance nitrogen and phosphorus reduction, and we discuss the importance of filler in constructed wetlands. Experiments revealed the adsorption capacity of the new filler: the maximum adsorption amounts of TP and NH3-N reached 0.47 g m-2 d-1 and 0.91 g m-2 d-1, respectively. The application potential of FA-SFe was verified in actual wastewater treatment, with the removal rates of ammonia nitrogen and TP reaching 71.3% and 62.7%, respectively. This study provides a promising pathway for nitrogen and phosphorus removal from rural tailwaters
Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning
Relational graph neural networks have garnered particular attention to encode
graph context in knowledge graphs (KGs). Although they achieved competitive
performance on small KGs, how to efficiently and effectively utilize graph
context for large KGs remains an open problem. To this end, we propose the
Relation-based Embedding Propagation (REP) method. It is a post-processing
technique to adapt pre-trained KG embeddings with graph context. As relations
in KGs are directional, we model the incoming head context and the outgoing
tail context separately. Accordingly, we design relational context functions
with no external parameters. Besides, we use averaging to aggregate context
information, making REP more computation-efficient. We theoretically prove that
such designs can avoid information distortion during propagation. Extensive
experiments also demonstrate that REP has significant scalability while
improving or maintaining prediction quality. Notably, it averagely brings about
10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and
takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.Comment: Accepted by IJCAI 202
ERNIE-mmLayout: Multi-grained MultiModal Transformer for Document Understanding
Recent efforts of multimodal Transformers have improved Visually Rich
Document Understanding (VrDU) tasks via incorporating visual and textual
information. However, existing approaches mainly focus on fine-grained elements
such as words and document image patches, making it hard for them to learn from
coarse-grained elements, including natural lexical units like phrases and
salient visual regions like prominent image regions. In this paper, we attach
more importance to coarse-grained elements containing high-density information
and consistent semantics, which are valuable for document understanding. At
first, a document graph is proposed to model complex relationships among
multi-grained multimodal elements, in which salient visual regions are detected
by a cluster-based method. Then, a multi-grained multimodal Transformer called
mmLayout is proposed to incorporate coarse-grained information into existing
pre-trained fine-grained multimodal Transformers based on the graph. In
mmLayout, coarse-grained information is aggregated from fine-grained, and then,
after further processing, is fused back into fine-grained for final prediction.
Furthermore, common sense enhancement is introduced to exploit the semantic
information of natural lexical units. Experimental results on four tasks,
including information extraction and document question answering, show that our
method can improve the performance of multimodal Transformers based on
fine-grained elements and achieve better performance with fewer parameters.
Qualitative analyses show that our method can capture consistent semantics in
coarse-grained elements.Comment: Accepted by ACM Multimedia 202
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