This series of articles takes an in-depth look at the OpenCV DNN module and its two core applications in the field of deep learning: image classification and target detection. As a powerful computer vision tool, the OpenCV DNN module not only provides efficient access to deep learning models, but also supports loading pre-trained models from different frameworks, making it an ideal choice for learning and implementing computer vision applications.
Introduces the concept of image classification and shows how to use the DenseNet121 model trained based on the Caffe framework for classification. This model is pre-trained on the ImageNet dataset and covers 1000 categories, providing a strong foundation for various image classification tasks. With clear steps, we explain how to load class names, pre-train models, and how to preprocess and infer images. Turning to object detection, the key steps and considerations in the process are explained. The MobileNet SSD model trained based on TensorFlow is used, which is popular for its advantages in speed and computational efficiency. We discussed how to use models trained on the MS COCO dataset to detect a variety of objects in images, including people, cars, and everyday objects.