Object detection via a multi-region & semantic segmentation-aware CNN model - gidariss/mrcnn-object-detection. Object detection via a multi-region & semantic segmentation-aware CNN model - ...

In this project, I have fine-tuned a Faster R-CNN model for object detection using a custom dataset. Faster R-CNN is a state-of-the-art object detection algorithm that combines deep learning with ...

The model training is based on the Common Object in Context (COCO) Dataset. The dataset has around 164K images based on 80 categories, also called as classes. Thus, this object detection model takes ...

Multiple object detection is a key challenge in object detection. Feature extraction and occlusion handling are two key elements in multiple object detection. However, existing methods do not perform ...

The developed moving object detection and classification using SAR images with an ensemble model is implemented in MATLAB, and the results are verified. Moreover, the ensemble method is computed over ...

The CNN first extracts features from the image using a series of convolutional and pooling layers and then uses these features to make predictions about the presence of different objects.

Our CNN design enables end-to-end training and real-time speeds while maintaining high average precision. The CNN trains on full images and directly optimizes detection performance. This unified model ...