Author: Zen and the Art of Computer Programming
Nowadays, artificial intelligence (AI) technology has become a major breakthrough in various industries, and its application scope is becoming wider and wider. However, in the industrial field, especially in the agricultural field, artificial intelligence technology still has severe technical challenges. There are two main categories of automated detection technologies:
- Object detection technology in the field of image processing and computer vision (CV) , including object classification, tracking, key point detection , etc. Traditional object detection methods such as Haar feature detection, HOG feature detection, SVM support vector machine classifier , etc., the accuracy of these methods is generally relatively high, but they will have relatively high accuracy in the face of image cluttered background, occlusion, posture changes, etc. large error;
- Target detection technologies in the field of deep learning (DL), such as YOLO, SSD, Faster RCNN, etc., can better solve the limitations faced by the above methods and achieve higher accuracy. However, due to its complexity, demand for computing resources, and the amount of training data required , its effectiveness still attracts attention. Therefore, how to quickly and cost-effectively apply DL methods for automatic detection has become an important topic.
In order to realize the automatic detection technology, the enterprise community tends to choose the SDK or API provided by the third party at present. However, these platforms are often difficult to meet the different types of detection needs of users. For example, if users need to identify defects on industrial products, they need to use multiple detection models at the same time, and each model is designed with corresponding parameter settings for different tasks. In addition, for the detection system, accuracy is an important indicator. However, existing detection platforms cannot guarantee a high enough accuracy rate. Therefore, how to combine the advantages of the DL method and effectively use various detection models to improve the accuracy of automated detection has become a top priority.
2. Explanation of basic concepts and terms
This article will cover the following related terms