Analysis of Vehicle Identification Classification YOLOv3

Performance Analysis of Vehicle Identification and Classification with YOLOv3

Implementation Plan:
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Step 1: Initially, we Load and collect the Vehicle Detection image dataset.

Step 2: Then, we Pre-process the collected data.

Step 3: Next, we implement the YOLOv3 algorithm to Detect speed of vehicles.

Step 4: Next, we Optimize the Data using Convolutional Neural Network (CNN) deep Learning Algorithm.

Step 5: Finally, Performance Metrics will be plotted for the following:

5.1: Number of Epochs vs Precision(%)

5.2: Number of Epochs vs Accuracy(%)

5.3: Number of Epochs vs F1_Score(%)

5.4: Number of Epochs vs Recall (%)

Software Requirements:
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1. Development Tool: Python – 3.11.4 or above

2. Operating System: Windows 10 (64-bit) or above

Dataset Link :
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https://www.kaggle.com/datasets/pkdarabi/vehicle-detection-image-dataset

Note :-
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1) If the above plan does not satisfy your requirement, please provide the processing details, like the above step-by-step.

2) Please note that this implementation plan does not include any further steps after it is put into implementation.

3) If the plan satisfies your requirement, Please confirm with us.

4) Project based on Simulation only, not a real time project.

5) Please understand that any modifications made to the confirmed implementation plan will not be made before or after the project development.