In this project, I demonstrate the fine-tuning process using a custom vehicle detection dataset. The dataset includes images of various vehicles, and our goal is to train the Faster R-CNN model to ...

It aims to enable object detection on microcontrollers in the power domain of milliwatts, with less than 0.5MB memory available for storing convolutional neural network (CNN) weights. The proposed ...

This paper proposes an anchorless fixed-frame remote sensing image object detection method using CNN and transformer, and the detection head based on centroid localization and boundary-aware vectors.

The object recognition part in Fast R-CNN is redundant for our system which can be removed to speed up the training process. In the experiments, we test our method by using SHRP 2 NDS database [10] ...

There can be various approaches to perform object localization in any object detection procedure. Using sliding filters of different sizes on the image to ... Basic R-CNN is very slow in training and ...

Contribute to Sibozhu/MotionBlur-detection-by-CNN development by creating an account on GitHub. Skip to content. Navigation Menu ... We generated the training data using images from thePascal Visual ...

Otherwise, it will yield a “no object” as shown in the green box to the right of figure 1. Additionally, DETR can process overlapping objects without “anchor boxes” or “non-maximum suppression.” ...