In the field of machine vision, object detection is a critical task that involves accurately identifying and locating a specific target in an image or video. With the continuous development of artificial intelligence and deep learning, many advanced object detection methods have emerged. This article will introduce three common machine vision object detection methods, including traditional feature engineering-based methods, deep learning-based methods, and some hybrid methods that combine the advantages of both.
1. Target detection method based on feature engineering
The object detection method based on feature engineering mainly relies on manually extracting image features and using these features for target classification and localization. These methods typically include the following steps: selecting an appropriate feature extraction algorithm, building and training a classifier, searching for target objects using sliding windows and image pyramids, and finally targeting and classifying. Common features include Haar features, HOG features, etc. However, due to the need to manually design features and classifiers, the accuracy and robustness of these methods in complex scenarios are limited.
Second, the target detection method based on deep learning
Deep learning-based object detection methods automatically learn features and classifiers through neural networks, eliminating the need for manual feature design. The most well-known of these are methods based on convolutional neural networks (CNNs), such as Faster R-CNN, YOLO, and SSDs. These methods enable fast and accurate object detection in complex scenarios by dividing the image into grids and predicting the class and location of targets on each grid. With its powerful expression ability and big data-driven training method, deep learning method has become a mainstream method in the field of object detection.
3. Hybrid approach
To take full advantage of the advantages of both approaches, the researchers proposed a number of hybrid approaches. These methods often combine traditional feature engineering methods with deep learning methods to improve the accuracy and robustness of object detection. For example, a deep learning-based approach might use traditional feature extraction algorithms to generate candidate regions, and then leverage deep neural networks for target classification and localization. This hybrid method is not only faster in terms of detection speed, but also better in terms of accuracy and stability of object detection.
Conclusion: With the continuous development of machine vision technology, object detection methods are also constantly evolving and improving. Although the method based on feature engineering is surpassed by the deep learning method, it still has certain application value in some specific scenarios. The deep learning-based method has higher accuracy and robustness and has become the main method of object detection. At the same time, the emergence of hybrid methods has further improved the performance of object detection technology. In the future, with the advancement of technology, we can expect the emergence of more accurate and efficient object detection methods, further expand the application scope of machine vision, and bring more innovation and breakthroughs to various industries.