Have you ever wondered how image detection works? How is the process of detection and classification of various things carried out with the help of a camera? All these questions trace back to the word “Computer Vision“.
There has been a lot of hype surrounding computer vision. It is a subfield of AI that extracts information from images and other visual inputs. It deals with the sophisticated knowledge that computers can get from digital photos or movies. From an engineering standpoint, it aims to comprehend and automate operations that the human visual system can perform.
There are many image processing models to achieve computer vision, out of which an object detection algorithm called PP-YOLOv2 is a remarkable one, which has been built on PP-YOLO with several improvements. So to get a clear understanding of PP-YOLOv2, one needs to understand its predecessors and the way it was developed.
In this article, we will focus on the YOLO models and how PP-YOLO is derived from them, and to what degree it is similar to and different from them. Also, we will take a quick sneak into the structure of these detection models. We will also discuss the advantages and limitations of this model in detail in this article.
What is YOLO?
YOLO, which stands for You Only Look Once, is a method that provides real-time object detection using neural networks. This algorithm is well-liked for its accuracy and quickness. It has been applied in a variety of ways to identify animals, humans, parking meters, and traffic lights.
There are many other approaches to object detection, but all the other methods are incapable of detecting objects in a single algorithm, i.e., they are not real-time based.
This algorithm identifies and finds different things in a picture in real-time. The class probabilities of the discovered photos are provided by the object identification process in YOLO, which is carried out as a regression problem.
The YOLO method uses Convolutional neural networks (CNNs) to recognize objects in real-time. The approach needs only one forward propagation through a neural network to detect objects.
This indicates that a single algorithm run is used to perform prediction throughout the full image, which also is a real-time oriented run. We can predict multiple class probabilities and bounding boxes simultaneously using CNN.
The concept of object detection is to train machines to develop a sense of recognition by giving a large number of datasets as inputs during the training and classifying stages and testing them with new inputs to check whether the system can identify the object successfully or not.
What is PP-YOLOv2?
PP-YOLOv2 is known as a more practical object detector. We call it PP-YOLO because PP stands for PaddlePaddle, which provides the foundation for all of the experiments in this study.
PaddlePaddle is an open-source machine learning framework developed by Baidu, a Chinese search engine. It is on par with TensorFlow and Pytorch. PaddlePaddle includes the building blocks required to develop learning models.
The modular designs used by PP-YOLO make it easier for developers to quickly create various pipelines. End-to-end techniques for data augmentation, creation, training, optimization, compression, and deployment are offered by PP-YOLO.
Distributed training is supported by PP-YOLO as well. The final prediction is the dense prediction, which consists of the label, the confidence score for the forecast, and a vector with the center, height, and breadth of the predicted bounding box.
PP-architecture YOLOs are heavily influenced by YOLO4. The architecture of PaddleDetection is primarily composed of three categories:
- Backbone: A convolution neural network for generating features is located in the section called backbone. It has a classification model that has already been trained. It is ResNet50-vd in this instance.
- Detection Neck: Next, the ConvNet representations are combined and mixed to generate a pyramid of features using the Feature Pyramid Network (FPN).
- Detection Head: Detection Head determines the object’s prediction and bounding box.
YOLOv2 vs PP-YOLOv2
The object detector PP-YOLOv2 improves upon YOLOv2 in several ways:
- The FPN includes a Path Aggregation Network to create bottom-up pathways. FPN (Feature Pyramid Network) is a feature extractor that produces proportionally scaled feature maps at several levels in a completely convolutional manner from a single-scale image of any size.
- It makes use of Mish Activation Functions. It is employed in YOLOv4 because of its inexpensive cost and unique characteristics, including its smooth and non-monotonic nature and unbounded above, bounded below the property, which enhances the performance.
- The input volume has increased. A soft label format is used to calculate an IoU-aware branch.
Significance of PP-YOLOv2
The main significance of PP-YOLOv2 is that it primarily aims to combine multiple already-used techniques that rarely increase the number of model parameters and FLOPs, to improve the detector’s accuracy as much as possible while keeping the speed nearly the same. The term FLOPS describes how many floating point operations a computing device can carry out in a single second.
In PP-YOLOv2, we increase the PP-performance of YOLOs from 45.9% mAP to 49.5% mAP by combining several efficient enhancements. PP-YOLOv2 operates at 68.9FPS with a 640×640 input size. The training batch size has increased from 64 to 192, and the Darknet53 backbone of YOLO v3 has been replaced with a ResNet backbone (as a mini-batch size of 24 on 8 GPUs).
Instead of putting forth a fresh detection model, PP-YOLO develops an object detector that is reasonably balanced in its efficacy and can be used immediately in real-world application settings.
The inference speed for the PP-YOLO is 72.9 FPS, which is faster than the YOLO v4’s inference speed of 65 FPS. The primary cause of this speed gain, according to the authors of PP-YOLO, is the superior optimization of tensorRT on the ResNet model compared to Darknet.
Issues with PP-YOLOv2
Even though it has overcome a lot of problems faced in the other detection models, there are some minor issues with PP-YOLOv2 that are addressed below, citing the reasons why it is a disadvantage in the first place and how it affects the image detection procedure. The disadvantages of this model serve as a target for future models that can become successful once they achieve it.
- Because each grid can only detect one object, performance suffers when there are clusters of little objects. These clusters of many tiny objects fall under many grids making the decision more difficult.
- Because the ratio of the bounding box is entirely learned from data, the main error of YOLO is from localization, and PP-YOLOv2 makes errors on the uncommon ratio of the bounding box. The localization and the uncommon ratio of the bounding box are major disadvantages to this model.
As we saw that PP-YOLOv2 is based on YOLOv3, the upgradations of YOLOv3, YOLOv4, and YOLOv5 namely cleared the issues faced by its predecessors. Hoping that the achievements of the successive models will serve as a good conclusion to this article, let’s take a quick look at them.
YOLOv4 incorporates the latest BoF (bag of freebies) and various BoS (bag of specials). Without lengthening the inference time, the BoF increases the detector’s accuracy. They merely drive up the price of training.
The two main advancements in YOLO V5 are bounding box anchors that learn automatically and mosaic data augmentation.
So these are technical upgrades of the successors. I hope this article helped you get a good understanding of the backdrop of YOLO models and object detection. Happy coding!