Experimental results of different detection methods.

“FLOPs” indicates the number of floating-point operations per second, and the unit is GM; “Parameter” indicates the number of parameters, and the unit is ×10M.</p

Experimental results of different component.

IFEM+Backbone+CCFM+ACDM demonstrates that the model does not use any local attention guidance layer; IFEM+Backbone+LAG+FPN+ACDM indicates that the FPN is used to replace the CCFM component; IFEM+Backbone+LAG+FPN+ACDM indicates that the PAFPN is used to replace the CCFM component. ‘Backbone’ indicates the feature extractor of SWin-transformer.</p

Experimental results for each category on different datasets of TTsports, RGBsports and FLIRs.

“Classes” indicates the object class in this datasets. “AP” represents the average detection accuracy of the objects. “ClassID” indicates the ID number of the category in the dataset.</p

Experimental results of different loss function.

ζMCE indicates that only cross-entropy loss is used; ζFL indicates that only Focal loss is used to adjust and optimize the entire network; ζMCE + ζFL indicates that simple weighted loss is used, that is, cross-entropy and Focal loss are used together to act on the network.</p

The detection effect of ours model on different dataset.

(a) indicates the datasets of TTsports. (b) indicates the datasets of FLIRs. Different color detection boxes in the image represent different object classes.</p

Fig 1 -

(a) The overall network structure of LAGSwin; (b) the network structure of the LAG module. Where ACDM represents the detection module of aligned convolution filters; CCFM represents the cross-fusion strategy; LAG represents the local attention guidance module; Stages 1 to 4 represent the four stages of Swin-transformer; P2 to P5 represent the top-down Pyramid structure features; T2 to T5 represent pyramid structure features from low to upper; ⊕ represent feature fusion; ⊗ represent matrix multiplication; sigmoid(⋅) represents activation function.</p

This figure consists of four kinds of curves, which respectively represent the failure rate of VM migration events of Random-Migration, StdPSO, PS-ABC and PS-ES.

<p>X axis denotes the number of VM migration requests. Y axia denotes the failure rate in VM migration events. The failure rate is equal to that the failure number of VM migration events is divided by the number of VM migration requests.</p

This figure has two kinds of bars, which respectively represent the failure rate on VM migration events in PS-ES with fixed evaporator factor and self-increased evaporator factor.

<p>This figure has two kinds of bars, which respectively represent the failure rate on VM migration events in PS-ES with fixed evaporator factor and self-increased evaporator factor.</p

This figure has three kinds of bars, which respectively represent the percentage of increase on penalty cost due to SLA violation, energy consumption cost and total cost under varying power management policies of On/Off, Single-DSS, Multiple-SS and Single-SSS.

<p>This figure has three kinds of bars, which respectively represent the percentage of increase on penalty cost due to SLA violation, energy consumption cost and total cost under varying power management policies of On/Off, Single-DSS, Multiple-SS and Single-SSS.</p

This figure has three kinds of curves, which respectively represent the percentage of the incremental energy consumption of DAPSO, PS-ABC and PS-ES with the percentage of load in the hosts increasing.

<p>X axis denotes the percentage of load in the hosts. Y axis denotes the percentage of the incremental energy consumption.</p