Object detection has been witnessing a rapid revolutionary change in the field of computer vision. Its involvement in the combination of object classification as well as object localisation makes it one of the most challenging topics in the domain of computer vision. In simple words, the goal of this detection technique is to determine where objects are located in a given image called object localisation and which category each object belongs to, which is called object classification.
In this article, we list down the 8 best algorithms for object detection one must know.
Best Object Detection Algorithms in 2024
1. Fast R-CNN
Written in Python and C++ (Caffe), Fast Region-Based Convolutional Network method or Fast R-CNN is a training algorithm for object detection. This algorithm mainly fixes the disadvantages of R-CNN and SPPnet, while improving on their speed and accuracy.
Advantages of Fast R-CNN: –
- Higher detection quality (mAP) than R-CNN, SPPnet
- Training is single-stage, using a multi-task loss
- Training can update all network layers
- No disk storage is required for feature caching
Click to download R-CNN paper in pdf
2. Faster R-CNN
Faster R-CNN is an object detection algorithm that is similar to R-CNN. This algorithm utilises the Region Proposal Network (RPN) that shares full-image convolutional features with the detection network in a cost-effective manner than R-CNN and Fast R-CNN. A Region Proposal Network is basically a fully convolutional network that simultaneously predicts the object bounds as well as objectness scores at each position of the object and is trained end-to-end to generate high-quality region proposals, which are then used by Fast R-CNN for detection of objects.
Click to download faster R-CNN towards real time object detection in pdf
3. Histogram of Oriented Gradients (HOG)
Histogram of oriented gradients (HOG) is basically a feature descriptor that is utilised to detect objects in image processing and other computer vision techniques. The Histogram of oriented gradients descriptor technique includes occurrences of gradient orientation in localised portions of an image, such as detection window, the region of interest (ROI), among others. One advantage of HOG-like features is their simplicity, and it is easier to understand the information they carry.
Check Intel resource and documentation to know more.
4. Region-based Convolutional Neural Networks (R-CNN)
The Region-based Convolutional Network method (RCNN) is a combination of region proposals with Convolution Neural Networks (CNNs). R-CNN helps in localising objects with a deep network and training a high-capacity model with only a small quantity of annotated detection data. It achieves excellent object detection accuracy by using a deep ConvNet to classify object proposals. R-CNN has the capability to scale to thousands of object classes without resorting to approximate techniques, including hashing.
Click to download Grishick Rich feature hierachies papers in pdf
5. Region-based Fully Convolutional Network (R-FCN)
Region-based Fully Convolutional Networks or R-FCN is a region-based detector for object detection. Unlike other region-based detectors that apply a costly per-region subnetwork such as Fast R-CNN or Faster R-CNN, this region-based detector is fully convolutional with almost all computation shared on the entire image.
R-FCN consists of shared, fully convolutional architectures as is the case of FCN that is known to yield a better result than the Faster R-CNN. In this algorithm, all learnable weight layers are convolutional and are designed to classify the ROIs into object categories and backgrounds.
Download Object Detection via Region-based Fully Convolutional Networks
6. Single Shot Detector (SSD)
Single Shot Detector (SSD) is a method for detecting objects in images using a single deep neural network. The SSD approach discretises the output space of bounding boxes into a set of default boxes over different aspect ratios. After discretising, the method scales per feature map location. The Single Shot Detector network combines predictions from multiple feature maps with different resolutions to naturally handle objects of various sizes.
Advantages of SSD: –
- SSD completely eliminates proposal generation and subsequent pixel or feature resampling stages and encapsulates all computation in a single network.
- Easy to train and straightforward to integrate into systems that require a detection component.
- SSD has competitive accuracy to methods that utilise an additional object proposal step, and it is much faster while providing a unified framework for both training and inference.
Download Single Shot MultiBox Detector
7. Spatial Pyramid Pooling (SPP-net)
Spatial Pyramid Pooling (SPP-net) is a network structure that can generate a fixed-length representation regardless of image size/scale. Pyramid pooling is said to be robust to object deformations, and SPP-net improves all CNN-based image classification methods. Using SPP-net, researchers can compute the feature maps from the entire image only once, and then pool features in arbitrary regions (sub-images) to generate fixed-length representations for training the detectors. This method avoids repeatedly computing the convolutional features.
Download Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition
8. YOLO (You Only Look Once)
You Only Look Once or YOLO is one of the popular algorithms in object detection used by researchers around the globe. According to the researchers at Facebook AI Research, the unified architecture of YOLO is extremely fast in manner. The base YOLO model processes images in real-time at 45 frames per second, while the smaller version of the network, Fast YOLO processes an astounding 155 frames per second while still achieving double the mAP of other real-time detectors. This algorithm outperforms the other detection methods, including DPM and R-CNN, when generalising from natural images to other domains like artwork.
Click to download Redmon YOLO CVPR papers in pdf
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