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Computer Vision Development – Recognition And Segmentation In Object Detection


Technology has been advancing at an exponential rate and so is the computational power of our systems. One field that has benefited from this boost is the field of computer vision development. A lot of computational power is required to train object detection models to achieve high mean average precision and recall for a given model. In this blog, we will look at how computer vision development in object detection works and also discuss image recognition and image segmentation.

Object Detection

Object detection involves classification and localization of the object, that is identifying an object in an image and also defining an area around the object to determine its position in the respective image. As can be seen in the image below deep learning models can be trained to detect different types of objects in an image or a video. Detection models are trained on images along with the corresponding labels. After training the model makes predictions of classifications and draws bounding boxes on validation and test images.

Image Recognition

Image recognition is another area of computer vision in which the task of the model is

Image-Recognition computer vision development services
limited to object classification and tagging. In image recognition, a deep learning model is extensively trained on the image dataset with labels and after training the model makes predictions about the presence of a class in each image. Lenet-5 deep learning network was developed to recognize numeric values in the image. The architecture used an image of 32x32x1 as input and then applied convolution and average pooling till the image size was 5x5x16 where 5×5 is the width and height of the image and 16 is the number of channels. Later this block was passed through 2 fully connected layers to get the prediction of a number in the image. Lenet-5 architecture developed by Yann LeCun for digit recognition is shown below.
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Iris recognition is now being implemented in smartphones that can reduce security breaches. It helps in medical imagery where different patterns are recognized to detect various types of diseases. One of the important aspects in which image recognition has been helpful is the educational system. Image recognition can aid the special needs students whether they are visually impaired or dyslexic with the use of text to speech applications based on image recognition. Supervised learning algorithms are generally used in image recognition problems. The algorithm is fed with good and bad examples of the dataset and the performance of the algorithm is evaluated on the test examples. The most popular way of solving image recognition problems is the use of deep learning models in which there are multiple hidden layers that extract features from the images and update the weights of the neural network to perform image recognition tasks. The downside of using deep learning is that a huge number of images are required to train the model. A minimum number of images to train a new model requires 1000 images per class. Researchers have countered this problem by implementing a technique known as transfer learning where a pre-trained model trained on another dataset is used to do image recognition on images of a new class by training it on our dataset. This method is less computationally expensive.

Steps to perform image recognition

The process required to perform image recognition tasks includes
  • Dataset acquisition.
  • Dataset pre-processing
  • Model configuration
  • Model training
  • Model evaluation
1. Dataset acquisition
First and foremost, the task is to acquire a dataset with a suitable number of examples per class. A model trained on a scarce number of examples will not yield optimal results.
2. Dataset pre-processing/Augmentation
Preprocessing is an essential step in making the model more robust when working in real-world situations and helps to avoid the model from overfitting. Some techniques that are used in preprocessing are image translation, rotation, mirroring, noise insertion, saturation, brightness, contrast, hue, and so on.
3. Model Configuration
Model configuration is another vital step. In this, we define the number of layers in the model. What will be the filter size in the convolution, size of stride, padding, number of epochs, learning rate, weight decay rate etc.
4. Model Training
After configuration, model training is done on the dataset. The speed of training is dependent on the size of the dataset and the computational capabilities of the system.
5. Model Evaluation
There are different performance metrics on which a model can be evaluated. Precision, recall, false alarm rate, balanced classification rate, mean average precision, and others. Model evaluation is performed on validation and test dataset.

Image Segmentation

Conventional object detection involves generating a bounding box around the object in each image or a video but in image segmentation, the classification and detection are not performed using rectangular bounding boxes, but it is done more precisely on a pixel level. Different classes are marked as a class according to their shapes thus giving us the information about a certain object’s shape which is not achievable in generic object detection.
Image segmentation is important where the margin of error is minuscule such as in health care industry in which finding out the severity of the cancer is determined by the shape of cancerous cells or in the Autonomous vehicle industry where the exact position of objects in the surrounding environment of the vehicle is necessary.
Image segmentation is further divided into two categories, one is semantic segmentation, and the other is instance segmentation.
Semantic segmentation detects objects in an image and groups them based on the predefined labels. In the example below the picture on the left side is of semantic segmentation where every person is labeled in a single group.
Instance Segmentation is more robust than semantic segmentation. In this object of a single class is further categorized as can be seen in the right image. This process of labeling is computationally expensive, but it gives an edge when analyzing videos or images for computer vision-related tasks.

Image-segmentation computer vision development services


The selection of a method whether it is image recognition, image segmentation or object detection is dependent on the task at hand. If you want to find what object is present in an image and you have no concern about where that object is located inside the image then you will go for image recognition. If you also want to locate the object then you will opt for object detection and you can use image segmentation when you have to label a class to the lowest pixel resolution. The following image below shows the differences between different types of object detection and localization techniques.
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Algoscale finds the best solution for you, whether you need object tracking and detection, real-time emotion detection, surveillance system, face recognition implementation, or invoice segmentation and OCR data extraction. With our vast expertise in computer vision development services, we assist your business in improving your customer experience and automating business processes and thus, acquire relevant insights to make better business decisions. Our team of professionals can help you choose the right platform for computer vision development, develop apps, integrate the camera, and improve the efficiency of your processes by interfacing with other systems.