What is Object Detection?

The subfield of image recognition that deals with the localization and labeling of multiple objects in images or videos

Object detection is a computer vision technique that allows us to identify and locate objects in an image or video. With this kind of identification and localization, object detection can be used to count objects in a scene and determine and track their precise locations, all while accurately labeling them.

Why is object detection important?

Object detection has many applications in various domains, such as:

Security and surveillance: Object detection can help monitor and detect suspicious activities, such as intruders, weapons, or vehicles, in real-time.
Self-driving cars: Object detection can help autonomous vehicles perceive and navigate their surroundings, such as detecting traffic signs, pedestrians, lanes, or obstacles.
Healthcare: Object detection can help diagnose diseases, such as cancer or COVID-19, by analyzing medical images, such as X-rays, CT scans, or MRI scans.
Retail: Object detection can help optimize inventory management, product placement, or customer behavior analysis by detecting and counting products, shelves, or customers in a store.
Agriculture: Object detection can help improve crop yield, pest control, or animal welfare by detecting and counting plants, insects, or livestock in a farm.

How does object detection work?

Object detection is a challenging task that requires both high-level semantic understanding and low-level visual features of the objects. There are two main types of methods for object detection: one-stage methods and two-stage methods.

One-stage methods

One-stage methods aim to directly predict the bounding boxes and class labels of the objects in an image or video. They are usually faster and simpler than two-stage methods, but may have lower accuracy. Some examples of one-stage methods are:

YOLO (You Only Look Once): YOLO divides the input image into a grid of cells and predicts the bounding boxes and class probabilities for each cell. It then applies non-maximum suppression to remove overlapping boxes and keep only the most confident ones. YOLO is one of the fastest object detection methods, but may miss some small or overlapping objects¹
SSD (Single Shot MultiBox Detector): SSD uses a base network, such as VGG or ResNet, to extract feature maps from the input image. It then applies multiple convolutional layers with different scales and aspect ratios to predict the bounding boxes and class scores for each feature map. SSD also uses non-maximum suppression to filter out redundant boxes. SSD is faster than two-stage methods, but may have lower accuracy for small objects²
RetinaNet: RetinaNet is a one-stage method that addresses the problem of class imbalance between foreground and background objects. It uses a feature pyramid network to generate feature maps with different resolutions and scales. It then applies two subnetworks: one for predicting the bounding boxes and another for predicting the class scores. RetinaNet also introduces a focal loss function that focuses on hard examples and reduces the loss for easy examples. RetinaNet achieves comparable accuracy to two-stage methods, but is slower than other one-stage methods³

Two-stage methods

Two-stage methods first generate a set of candidate regions that may contain objects, and then classify and refine each region. They are usually more accurate and robust than one-stage methods, but are slower and more complex. Some examples of two-stage methods are:

R-CNN (Region-based Convolutional Neural Network): R-CNN uses a region proposal algorithm, such as selective search, to generate about 2000 candidate regions for each input image. It then extracts features from each region using a pre-trained CNN, such as AlexNet or VGG. It then applies an SVM classifier to predict the class label for each region. It also uses a linear regressor to adjust the bounding box coordinates. R-CNN is one of the first deep learning methods for object detection, but is very slow and inefficient⁴
Fast R-CNN: Fast R-CNN improves upon R-CNN by using a single CNN to extract features from the entire input image. It then applies a region of interest (RoI) pooling layer to crop and resize the features for each candidate region. It then applies a fully connected layer to predict the class scores and bounding box offsets for each region. Fast R-CNN is much faster than R-CNN, but still relies on an external region proposal algorithm⁵
Faster R-CNN: Faster R-CNN further improves upon Fast R-CNN by replacing the external region proposal algorithm with a region proposal network (RPN). The RPN shares the same feature extractor with the Fast R-CNN network and predicts the region proposals directly from the feature maps. The RPN also uses an anchor mechanism to generate multiple proposals with different scales and aspect ratios for each location on the feature map. Faster R-CNN is one of the most popular and accurate object detection methods, but is still slower than one-stage methods⁶

How to evaluate object detection models?

Object detection models are usually evaluated using two metrics: mean average precision (mAP) and average recall (AR).

mAP measures the accuracy of the model in predicting the correct class and location of the objects. It is computed by averaging the precision values across all classes and recall levels. Precision is the ratio of true positives to all positives, and recall is the ratio of true positives to all relevant objects. A higher mAP indicates a better model performance.
AR measures the coverage of the model in detecting all the objects in an image or video. It is computed by averaging the recall values across all classes and intersection over union (IoU) thresholds. IoU is the ratio of the area of overlap to the area of union between a predicted bounding box and a ground truth bounding box. A higher IoU indicates a more accurate localization. A higher AR indicates a better model performance.

Conclusion

Object detection is a subfield of image recognition that deals with the localization and labeling of multiple objects in images or videos. It has many applications in various domains, such as security, self-driving cars, healthcare, retail, or agriculture. Object detection methods can be categorized into one-stage methods and two-stage methods, depending on whether they directly or indirectly predict the bounding boxes and class labels of the objects. Object detection models are usually evaluated using mAP and AR metrics, which measure their accuracy and coverage in detecting the objects.

References

¹: You Only Look Once: Unified, Real-Time Object Detection ²: SSD: Single Shot MultiBox Detector ³: Focal Loss for Dense Object Detection ⁴: Rich feature hierarchies for accurate object detection and semantic segmentation ⁵: Fast R-CNN ⁶: Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

What is Object Detection?