Smart target detection - Pytorch builds YoloV7-3D monocular image target detection platform

Smart target detection - Pytorch builds YoloV7-3D monocular image target detection platform

• study preface
• Source code download
• YoloV7-3D improved part (incomplete)
• YoloV7-3D implementation ideas
• • 1. Overall structure analysis
  • 2. Analysis of network structure
  • • 1. Introduction to backbone network Backbone
    • 2. Construct FPN feature pyramid for enhanced feature extraction
    • 3. Use Yolo Head to obtain prediction results
  • 3. Decoding of prediction results
  • • 1. Obtain prediction frame and score
    • 2. Score screening and non-maximum suppression
  • 4. Training part
  • • 1. Calculate the content required for loss
    • 2. The matching process of positive samples
    • • a. Match the prior frame and feature points
      • b. SimOTA adaptive matching
    • 3. Calculate Loss
• Train your own YoloV7-3D model
• • 1. Data set preparation
    • • 1. Modify the data set loading format
    • • 1. Dataloader data loading modification
    • • 1. Mosaic data enhancement of 3D targets
  • Second, the processing of data sets
  • 3. Start network training
  • 4. Prediction of training results
• FAQ summary

study preface

The YOLOv7-3D target detection algorithm is implemented based on the Rope3D dataset.

Source code download

To be perfected

Dataset visualization

YOLOv7-3D detection effect

The effect of training 30epoch

tutorial

Algorithm to be perfected