(NY) North Carolina, Raleigh (NC) North Dakota, Bismark (ND) Ohio, Columbus (OH) Oklahoma, Oklahoma City (OK) Oregon, Salem (OR) Pennsylvania, Harrisburg (PA) Rhode Island, Providence (RI) South Carolina, Columbia (SC) South Dakota, Pierre (SD) Tennessee, Nashville (TN) Texas, Austin...
Headquarters:(View Map) Nashville, TN, United States 441921Global Rank 83154 United States 116 KEstimated Visits Created with Highcharts 7.1.2JulyAugustSeptemberOctoberNovemberDecember010K20K30K40K50K60K70K80K90K100K110K120K Traffic Sources Search ...
this app is very much like the original Tdot map (which was really useful), except, if you just want to see the traffic around Nashville, you can’t because the view is completely blocked by the multiple camera buttons; they completely block out the highway underneath. If, in traffic, ...
Eight adjacent grid nodes (within the yellow line box in Figure 2) are randomly selected from the network map of ship traffic flow in the port. The correlation magnitude of each grid node in space is calculated using Equation (2) to extract the correlation of ship traffic flow in the port...
By considering varying recall levels, mAP provides insight into how well the detector performs at various sensitivity levels. The mAP is determined by Equation (6). 𝑚𝐴𝑃=1𝐾∑𝑘=1𝐾𝐴𝑃𝑘mAP=1K∑k=1KAPk (6) Intersection over Union (IoU) is another crucial metric ...
This noise reduction process selectively preserves the relevant traffic sign features while removing unwanted “feature noise” from the feature map. Consequently, the original feature map from the backbone can more effectively carry out the task of traffic sign detection following the integration of ...
Yolo V4 (No Flip) achieves the highest mAP, 81.22%, while training with IoU at 65.98% and a loss value of 0.429. Followed by Yolo V4-tiny (No Flip) with mAP 80.47% and IoU 63.79%. Next, Yolo V3 got a mAP value of 78.31% and an IoU of 58.62%, as shown in Table 3. In ...
Figure 1. (a) Location of Nashville, TN, on US map; (b) RDS detector locations in Nashville, TN. The objective of this study is to represent the traffic condition of a massive network, and traffic speed is one of the most widely used and most intuitive characteristics to reflect the ...
The mAP of the trained model can be achieved and the results are displayed in Table 6. As can be seen from Table 6, compared with the original YOLOv4 algorithm, the mAP of the improved algorithm increases by 8.04% and the model size decreases by 81.97%. The results confirm that each ...
Several changes were made to improve the system’s overall effectiveness (mAP). The system’s efficacy in decreasing the negative effects of traffic congestion was improved not just by enhancing the accuracy but also by lowering the time required to count cars. By performing speed calculations, ...