研究目的
To complete the target detection task in the scene of a few target samples and low configuration software running environment.
研究成果
The IHATMAS algorithm provides fast and accurate target detection with high resistance to unfavorable conditions like illumination changes and obstruction, making it competitive for real applications.
研究不足
The algorithm may require parameter tuning for different scenarios, and its performance could be affected by extreme noise or very large image sizes not tested.
1：Experimental Design and Method Selection:
The algorithm integrates Chamfer distance transformation and edge direction features for similarity measurement, uses multiple random templates and SVM for classification, and employs a pyramid hierarchical search strategy for efficiency.
2：Sample Selection and Data Sources:
Test images include production line images, PCB images, and scattered workpieces captured using a Basler camera.
3：List of Experimental Equipment and Materials:
Basler acA640-120gm camera, Computar M0814-MP2 F
4：4 F12 lens, Intel Core i5-6500 CPU, 4GB memory, Windows 7 OS, Microsoft Visual Studio Experimental Procedures and Operational Workflow:
Pre-treat images to extract edges, build pyramids, perform template matching from top to bottom levels using SVM and random templates, and verify results under various conditions.
5：Data Analysis Methods:
Compare matching accuracy and time complexity with Vision Sensor software, using statistical measures for error and duration.
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