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.

翻译：计算机视觉的主要问题是检测背景杂乱遮挡环境中的小型、不确定移动物体或目标，这严重影响了深度学习模型的检测精度。为解决这些问题，我们重点研究采用SSD和YOLO算法的深度学习模型，用于在背景杂乱遮挡环境中实时检测汽车和坦克，以提高检测精度并减少这些模型面临的难题。所提方法构建了自定义数据集，并采用预处理技术清理含噪数据集。为训练所提模型，我们应用数据增强技术来平衡和多样化数据。通过应用这些技术对模型进行微调、训练和评估，并在建立的数据集上验证结果，我们获得了比未采用这些技术时更高的精度。SSD-Mobilenet v2模型的精度和帧率均高于YOLO V3和YOLO V4。此外，通过采用数据增强、降噪、参数优化和模型融合等多种技术，我们提升了检测与识别的有效性。我们还增加了计数算法、目标属性实验对比，并为所提模型开发了具备目标计数、警报、状态、分辨率和帧率功能的图形用户界面系统。最后，为验证所提方法的重要性，我们对YOLO V3、V4和SSD进行了分析，从而完整实现了所提方法。