Description:-
Object detection is a computer vision technique used to identify and locate objects in an image or video. It involves detecting objects such as cars, people, furniture, animals, and other objects in an image or video. It uses a combination of techniques such as image processing, pattern recognition, deep learning, and machine learning to detect objects.
Object detection is the process of locating objects with bounding boxes in an image or a video. Object detection algorithms can be used for tasks such as image classification, object tracking, and scene understanding. This tutorial will make you learn how to detect objects using deep learning and OpenCV.
MobileNet SSD
MobileNet is a small, efficient convolutional neural network (CNN) designed specifically for mobile and embedded vision applications. It is based on a streamlined architecture that uses depth-wise separable convolutions to build light-weight deep neural networks. MobileNet has been trained on the ImageNet dataset and can classify images into 1000 different categories. MobileNet can be used for image classification, object detection, semantic segmentation, and other computer vision tasks.
Single Shot object detection (SSD)
Single Shot object detection (SSD) is a computer vision technique for detecting objects in an image or video frame. It uses a single deep neural network to predict both the bounding box coordinates and class probabilities for each object in an image. The SSD approach is based on a feed-forward convolutional network that produces a fixed-size collection of bounding boxes and scores for the presence of object class instances in those boxes.SSD is designed to be independent of the base network, and so it can run on top of any base networks such as VGG, YOLO, MobileNet.
SSD comprises two parts:
1. Extract feature maps
2. Apply convolution filter to detect objects
To further tackle the practical limitations of running high-resource and power-consuming neural networks on low-end devices in real-time applications, MobileNet was integrated into the SSD framework. So, when MobileNet is used as the base network in the SSD, it becomes MobileNet SSD.
The MobileNet SSD method was first trained on the COCO dataset and was then fine-tuned on PASCAL VOC reaching 72.7% mAP (mean average precision)
Stepwise Guide to Object Detection in Images
Step 1: Download pre-trained MobileNetSSD Caffe model and prototxt.
Use a MobileNet pre-trained downloaded from the link given below.
https://github.com/chuanqi305/MobileNet-SSD/ that was trained in Caffe-SSD framework.
Download the pre-trained MobileNet SSD model and prototxt from:
MobileNetSSD_deploy.caffemodel
MobileNetSSD_deploy.prototxt
Step 2. Import Libraries & Define Model
Define pretrained detection model with frozen model and configuration file. Also, download label files to give label to object.
Step 3. Define model parameters.
Step 4. Detect object with sample image.
Step 5. Locate object in image.
Detect the location of objects in the image and give a location to them. After that, put rectangle boxes and text.
Step 6. Use the following command to show the output image.
Output:-
Input Image Output Image
Note: The person and car are detected with this image successfully.
Step 7. After this, do the same thing for the video to find out the objects.
Output :-
Conclusion :-
To conclude, detecting an object with OpenCV-Python is a relatively easy and straightforward process. By understanding the different methods of object detection, you can quickly and accurately detect objects in an image using OpenCV-Python. Additionally, various methods of object tracking such as optical flow, contour tracking and Kalman filters can be used to further enhance the accuracy of the detection process.
Checkout Next –A Guide to Face Detection in Python
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