Mastering Object Detection: From R-CNN to YOLO and Real-World AI Applications

Computer vision aims to give machines the ability to "see", ranging from simple image classification to 3D pose estimation.

1. Common Object Detection Models

1.1 R-CNN

R-CNN uses selective search to generate region proposals (ROIs), then extracts CNN features for each ROI, trains a classifier and a bounding‑box regressor.

Select potential ROIs

Train a strong feature extractor

Train the final classifier

Train a regression model to refine ROI coordinates

1.2 Fast R-CNN

Fast R-CNN addresses R-CNN’s inefficiencies by sharing convolutional layers across all ROIs and allowing end‑to‑end training, eliminating the need for separate SVM and regression steps.

1.3 Faster R-CNN

Faster R-CNN adds a Region Proposal Network (RPN) that directly predicts proposals, removing the dependence on external methods like selective search. The RPN is trained first, then Fast R-CNN is fine‑tuned using the generated proposals.

Pre‑train an ImageNet model and train the RPN

Use the RPN proposals to train Fast R-CNN

Initialize RPN with the Fast R-CNN backbone and fine‑tune RPN layers

Freeze early convolutional layers and train the Fast R-CNN fully connected layers

1.4 YOLO

YOLO abandons proposal‑based detection, dividing the input image into an S×S grid and predicting object presence, bounding box coordinates, and class probabilities for each cell.

1.5 SSD

SSD combines YOLO’s grid concept with anchor boxes from Faster R-CNN, using multi‑scale feature maps to predict classes and offsets, achieving fast and relatively accurate detection.

2. Industrial Applications of Object Detection

Alibaba’s AI technology has been applied to photovoltaic (PV) module quality inspection, replacing manual inspection with an EL‑based system that achieves over 95 % accuracy on both cell and module levels, dramatically reducing costs and improving efficiency for manufacturers.

The solution handles challenges such as subtle dark patterns on cells and large variations in defect size, enabling stable online inspection for both monocrystalline and multicrystalline PV cells and modules.

Future collaborations aim to extend AI‑driven quality control to more industries, writing a new chapter in intelligent manufacturing.