Object Detection In 2022: The Definitive Guide Part 2

In our previous article, we talked about object detection in detail. In this article, we will explain in detail the advantages and disadvantages of deep learning methods and describe the most famous object detection algorithms.
Aylin Coskun
2 minutes

Object Detection In 2022: The Definitive Guide Part 2

In our previous article, we talked about object detection in detail. In this article, we will explain in detail the advantages and disadvantages of deep learning methods and describe the most famous object detection algorithms.

Both Advantages And Disadvantages of Image Processing Methods

Traditional (1) image processing methods or present (2) deep-learning networks can both be used for object detection.

You can check our latest article about differences between object detection and image recognition from here.

1. The majority of image processing methods are unsupervised and don't need previous data for training.
Pons: As a result, those operations do not call for manually labeled data from annotated photos (for supervised training).
Cons: These methods are only effective in complicated scenes(without a monochromatic background), occluded scenes (with partially hidden objects), with light and shadow, and scenes with clutter.

Both Advantages And Disadvantages of Deep Learning Methods

2. Most deep learning techniques rely on either supervised or unsupervised learning, with supervised techniques typically used for computer vision tasks. The compute capacity of GPUs, which is growing quickly every year, is what limits performance.

Pons: Deep learning object recognition is far more resistant to occlusion, complicated situations, and difficult illumination.

Con's: Image annotation is a time- and money-consuming procedure requiring significant training data. A tiny dataset would be categorizing 500 000 photos to train a unique DL object detection algorithm. But several benchmark datasets (MS COCO, Caltech, KITTI, PASCAL VOC, V5) make labeled data available.

Deep learning object detection is now extensively acknowledged by academics and used in developing commercial solutions by computer vision companies.

Most Popular Object Detection Algorithms

Convolutional neural networks (R-CNN, Region-BasedConvolutional Neural Networks), Fast R-CNN, and YOLO are popular object detection techniques (You Only Look Once). YOLO belongs to the single-shot detector family, whereas the R-CNNs are members of the R-CNN family. In the following sections, we shall describe the main features and variations of the most widely used object detection algorithms.

YOLO  - You Only Look Once

YOLO object detection uses a single neural network as the areal-time object detection system. The most recent version of Image AI, v2.1.0, now allows users to train their own YOLO models to recognize any kind and quantity of objects. Convolutional neural networks are examples of classifier-based systems where the system applies the detection model to an image at several scales and locations using repurposed classifiers or localizers. This method classifies some "high scoring" image portions as detections. Simply said, positive identification occurs in the zones that closely resemble the provided training images.

YOLO is substantially faster than most convolutional neural networks since it conducts classification and bounding box regression in a single step as a single-stage detector. For instance, YOLO object detection is 100 times quicker than Fast R-CNN and more than 1000 times faster than R-CNN.

On the MS COCO dataset, YOLOv3 achieves an mAP of 57.9% as opposed to DSSD513's 53.3 and RetinaNet's 61.1 percent. For training, YOLOv3employs multi-label classification using overlapping patterns. As a result, it may be used for object detection in complex circumstances. YOLOv3 can be used to classify small things due to its multi-class prediction capabilities. However, it performs worse when trying to detect significant or medium-sized items.

A better version of YOLOv3 is YOLOv4. The three primary innovations are cross mini-batch normalization, self-adversarial training, and mosaic data improvement.

Furthermore, you can check out our latest article about How to train YOLOV7 on Personal Protective Equipment Data?

SSD – Single-Shot Detector

Detectors that can predict many classes include the well-known SSD one-stage detector. In order to recognize objects in images using a single deep neural network, the method discretizes the output space of bounding boxes into a collection of default boxes over various aspect ratios and scales per feature map position.

When an object is present in a default box, the object detector assigns scores for each object category and modifies the default box to fit the object better. The network also integrates predictions from many feature maps with various resolutions to handle objects of various sizes.

R-CNN – Region-based Convolutional Neural Networks

Innovative methods for applying deep models to object detection include regions with CNN features (R-CNNs), also known as region-based convolutional neural networks. R-CNN models first choose a number of suggested regions from an image (anchor boxes are one form of the selection method, for instance) and then label the categories and bounding boxes of those selected regions (e.g., offsets). These labels are produced using the program's pre-defined classes. After that, they undertake forward computation to extract features from each suggested area using a convolutional neural network.

The inputted image is first segmented into about two thousand region parts in R-CNN, then a convolutional neural network is applied to each region individually. When the regions' sizes are calculated, the correct region is added into the neural network. It follows that an approach that is that specific could result in time restrictions. YOLO classifies and generates bounding boxes independently, and a neural network is applied to one region at a time. As a result, training time is noticeably longer than with YOLO.

Fast R-CNN was created in 2015 to reduce train time drastically. Fast R-CNN runs the neural network once on the entire image as opposed to the original R-two CNN's thousand regions of interest, which independently computed the neural network features. Although the architecture of YOLO and this are quite similar, YOLO is still faster than FastR-CNN due to the ease of the code.

A unique technique called Region of Interest (ROI) Poolingis used at the network's conclusion to slice out each Region of Interest from the output tensor, reshape it and classify it. Fast R-CNN is, therefore, more accurate than the original R-CNN as a result. This recognition method requires fewer data inputs to train Fast R-CNN and R-CNN detectors.

Mask R-CNN

Fast R-CNN has been improved by Mask R-CNN. The key distinction between the two is that Mask R-CNN introduced a branch for object mask prediction and a branch for bounding box detection. Faster R-CNN is slightly slower, but Mask R-CNN can run at five frames per second and is easy to train.


SqueezeDet is the name of a deep neural network for computer vision that was introduced in 2016. SqueezeDet was explicitly created for autonomous driving, where it uses computer vision techniques to do object detection. It is a single-shot detection algorithm, similar to YOLO. Convolutional layers are only utilized to extract function maps in SqueezeDet, but they are also employed as the output layer to compute bounding bins and sophistication odds. SqueezeDet models' detection pipelines only include single forward runs of neural networks, which makes them incredibly rapid.


Object detection tasks are carried out using the mobile net, a single-shot multi-box detection network. The Caffe framework is used to implement this model. As previously mentioned, the model output is a standard vector that contains the tracked object data.


An evolutionary object detector called YOLOR was unveiled in 2021. The algorithm simultaneously uses implicit and explicit information to model training. As a result, YOLOR can acquire a broad representation and use it to carry out a variety of operations.

With the help of multi-task learning, kernel space alignment, and prediction improvement, implicit knowledge is incorporated into explicit knowledge. This technique dramatically enhances the performance of object detection for YOLOR.

Object Detection with Cameralyze

Object detection is one of the most basic and challenging problems in computer vision. Recently, it has attracted a lot of attention, especially in light of the popularity of deep learning techniques that have taken over as the leading modern detection methods. Using Cameralyze, you can quickly and immediately decide depending on what you observe due to the promptness and accuracy of the responses (98.64 %).

Here is the guide for you to build Object Detection Application on Cameralyze Platform:

You can save time and money due to the lack of technical know-how and labor requirements. So the time is to check what Cameralyze is capable of with this technology. You can start free now!    

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