Real-Time Object Detection in Occluded Environment with Background
  Cluttering Effects Using Deep Learning
Syed Muhammad Aamir Hongbin Ma Malak Abid Ali Khan Muhammad Aaqib
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.