To train our model, we use images in Training Set, and the Testing set contains the images used for performance evaluation. Abstract: Object Detection is an emerging ... approach Convolutional Neural ...

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 ...

Before YOLO, Fast R-CNN was one of the most popular object detection algorithms that ... some issues during training. The YOLOv7 algorithm plans on using the concept of gradient propagation path to ...

The proposed algorithm generates a “confidence score” for each frame to check the trustworthiness of the BB generated by the CNN detector ... positions of the weed for perfect detection. When ...

And for this, we need to use modern algorithms and architecture based neural network models with much better object detection accuracy ... the proposed model from confusion matrices. In this study, ...

Abstract: Object Detection is one of the important entities in the field of Computer Vision with a large number of applications. This project demonstrates Object ...

Going in the case of object detection where CNN is very helpful ... similarity between the regions. Using this algorithm, the model continues to merge all the regions together to improve the size of ...

Underwater object detection currently faces ... it can be found that using RepVGG modules can improve the detection accuracy of the model from 84.0% in the original YOLOv5s algorithm to 84.84%.