Editor's Note: tryo.labs is a technology support company specializing in machine learning and natural language processing. In this article, the company’s researchers introduce Faster R-CNN, an advanced object detection tool used in their research process. The article covers its construction, implementation principles, and how it works in practice.
Previously, we introduced what object detection is and how it is applied in deep learning. Last year, we decided to dive deeper into Faster R-CNN. By reading the original paper and related references, we gained a clear understanding of its inner workings and deployment methods.
Finally, we implemented Faster R-CNN on Luminoth, a computer vision toolkit built on TensorFlow. We shared our findings at the European and Western Conferences of the Open Data Science Conference (ODSC), where they received significant attention. Based on our research and development efforts with Luminoth, we believe it’s important to document these insights for our readers.
Background
Faster R-CNN was first introduced at NIPS 2015 and has since undergone several revisions. It is the third iteration of the R-CNN series developed by Ross Girshick’s team. In 2014, the first R-CNN paper, "Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation," used a selective search algorithm to generate regions of interest (RoIs) and then used a standard CNN to classify and refine them. In early 2015, R-CNN evolved into Fast R-CNN, introducing ROI Pooling, which improved efficiency by sharing computation across RoIs. Finally, Faster R-CNN was proposed as the first fully end-to-end model.
Structure
The architecture of Faster R-CNN is complex due to its multiple components. We’ll start with a general overview before diving into each part in detail.
The main goal of Faster R-CNN is to detect objects in an image and provide:
- A list of bounding boxes
- A label for each bounding box
- The probability of each label and bounding box
[Image: Introduce the target detection tool Faster R-CNN, including its construction and implementation principle]
Faster R-CNN Schematic
The input is an image represented as a tensor (height x width x depth). A pre-trained CNN processes the image, generating a feature map that serves as the basis for the next steps.
This technique is commonly used in transfer learning, where weights from large-scale datasets are used to train models on smaller ones.
Next comes the Region Proposal Network (RPN), which uses the CNN features to identify potential regions (bounding boxes) that may contain objects.
One of the biggest challenges in using deep learning for object detection is handling variable-length bounding box outputs. Most neural networks produce fixed-size outputs, but object detection requires variable-length lists. Anchors help solve this by placing fixed-size reference boxes across the image, reducing the problem to two tasks per anchor:
- Does this anchor contain an object?
- How should this anchor be adjusted to better fit the object?
After obtaining a list of candidate regions, the next step is to extract features using ROI pooling, which transforms the feature map corresponding to each region into a fixed-size vector.
Finally, the R-CNN module classifies the content within the bounding box and refines the coordinates to better match the object.
Although the description is high-level, this is essentially the workflow of Faster R-CNN. In the following sections, we will go into more detail about the architecture, loss functions, and training process.
Basic Network
As mentioned earlier, the first step involves using a pre-trained CNN (like VGG or ResNet) for classification. While this might seem straightforward, understanding how and why it works is key. Visualizing the output of intermediate layers also helps in gaining deeper insights.
There is no universally best network architecture. Early R-CNN used ZF and VGG, but newer architectures like MobileNet, ResNet, and DenseNet have emerged, offering better performance and efficiency.
VGG
To illustrate, let’s take VGG-16 as an example. It consists of multiple convolutional layers, each extracting higher-level features from the previous one. The final convolutional layer produces a feature map that encodes spatial information about the image while maintaining the relative positions of objects.
ResNet vs. VGG
Today, most systems use ResNet instead of VGG due to its superior performance and ease of training. ResNet introduces residual connections and batch normalization, making it easier to train deep networks.
Anchors
Anchors are fixed reference boxes placed across the image at different scales and aspect ratios. They help address the issue of variable-length bounding boxes by providing a framework for predicting offsets.
Region Proposal Network (RPN)
The RPN takes the convolutional feature map and generates proposals. For each anchor, it predicts two things:
- A score indicating whether it contains an object (objectness score)
- A set of four values to adjust the anchor to better fit the object
Training and Loss Function
During training, anchors are categorized based on their Intersection over Union (IoU) with ground-truth boxes. Foreground anchors (IoU > 0.5) are considered positive samples, while background anchors (IoU < 0.1) are negative. A mix of both is sampled to maintain balance.
Post Processing
Non-maximum suppression (NMS) is used to eliminate overlapping proposals. After NMS, the top N proposals are selected, typically around 2000.
ROI Pooling
Once proposals are generated, ROI pooling is used to extract fixed-size features from the convolutional feature map. This allows for efficient classification and regression.
R-CNN
The final stage of Faster R-CNN is the R-CNN module, which classifies the proposals and refines their bounding boxes. It uses fully connected layers to predict category scores and bounding box adjustments.
Training
Faster R-CNN is trained end-to-end, combining losses from the RPN and R-CNN. Training involves adjusting the weights of the base network, RPN, and R-CNN simultaneously.
Evaluation
Evaluation is done using metrics like mAP (mean average precision), with IoU thresholds set to determine correct detections.
Conclusion
Now you have a solid understanding of how Faster R-CNN works. If you want to explore further, you can check out the implementation in Luminoth.
Faster R-CNN demonstrated that deep learning could effectively tackle complex computer vision problems. Today, similar techniques are being used for semantic segmentation, 3D object detection, and more. Understanding these models is essential as we continue to push the boundaries of AI.
Turnkey PCBA Service,Turnkey PCBA Assembly Service,Turnkey PCBA Manufacturing,PCBA assembly quote
Dongguan Jinglin Communication Technology Co., Ltd. , https://www.jlpcba.com