Real-Time Object Detection in Occluded Environment with Background Cluttering Effects Using Deep Learning

TL;DR

This paper presents a deep learning approach using SSD and YOLO algorithms for real-time detection of occluded and cluttered objects like cars and tanks, with improvements in accuracy and speed through dataset enhancement and model optimization.

Contribution

The study introduces a custom dataset, preprocessing, data augmentation, and model fusion techniques to enhance real-time object detection in occluded environments, outperforming standard models.

Findings

SSD-Mobilenet v2 outperforms YOLO V3 and V4 in accuracy and FPS.

Data augmentation and noise reduction improve detection precision.

Graphical user interface facilitates object counting and alerts.

Abstract

Detection of small, undetermined moving objects or objects in an occluded environment with a cluttered background is the main problem of computer vision. This greatly affects the detection accuracy of deep learning models. To overcome these problems, we concentrate on deep learning models for real-time detection of cars and tanks in an occluded environment with a cluttered background employing SSD and YOLO algorithms and improved precision of detection and reduce problems faced by these models. The developed method makes the custom dataset and employs a preprocessing technique to clean the noisy dataset. For training the developed model we apply the data augmentation technique to balance and diversify the data. We fine-tuned, trained, and evaluated these models on the established dataset by applying these techniques and highlighting the results we got more accurately than without applying these techniques. The accuracy and frame per second of the SSD-Mobilenet v2 model are higher than YOLO V3 and YOLO V4. Furthermore, by employing various techniques like data enhancement, noise reduction, parameter optimization, and model fusion we improve the effectiveness of detection and recognition. We further added a counting algorithm, and target attributes experimental comparison, and made a graphical user interface system for the developed model with features of object counting, alerts, status, resolution, and frame per second. Subsequently, to justify the importance of the developed method analysis of YOLO V3, V4, and SSD were incorporated. Which resulted in the overall completion of the proposed method.