Summary of Real-time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning, by Syed Muhammad Aamir et al.
Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning
by Syed Muhammad Aamir, Hongbin Ma, Malak Abid Ali Khan, Muhammad Aaqib
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of detecting small, unknown moving objects or those partially occluded by cluttered backgrounds using deep learning models. The primary goal is to improve the accuracy of real-time object detection, specifically for cars and tanks. To achieve this, the authors employ SSD and YOLO algorithms, fine-tune their models using a custom dataset, and apply preprocessing techniques to reduce noise. Data augmentation is also used to balance and diversify the data. The results show that the SSD-Mobilenet v2 model outperforms YOLO V3 and V4 in terms of accuracy and frame per second. To further enhance detection effectiveness, various techniques are applied, including data enhancement, noise reduction, parameter optimization, and model fusion. Additionally, a counting algorithm is incorporated, along with target attributes experimental comparison, and a graphical user interface system for object counting, alerts, status, resolution, and frame per second. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve the problem of detecting small objects in busy scenes using computer vision. The goal is to make computers better at spotting things like cars and tanks that are moving or partially hidden. To do this, the researchers use special kinds of artificial intelligence called deep learning models. They also clean up some noisy data, add more variety to it, and test their models on a specific dataset. The results show that one model is better than others at detecting objects quickly and accurately. To make their model even better, they try different techniques like making the data less messy or adjusting some settings. Finally, they create a user-friendly system that lets people use these detection tools to count objects, get alerts, see how things are going, and more. |
Keywords
» Artificial intelligence » Data augmentation » Deep learning » Object detection » Optimization » Yolo
