3D object detection is a computer vision task that is used for various applications. However, it has several drawbacks due to occlusion, noise, inefficient field of view, etc. In addition, the recent LiDAR point clouds-based 3D object detection methods lack sparsity and homogeneity which affects the detection accuracy. In this paper, we detect 3D objects based on static and dynamic states by performing preprocessing, segmentation, extraction of features, classification, and regression considering RGB-D images, and LiDAR point clouds. Firstly, the quality of the image is enhanced by performing preprocessing which consists of two levels. In the first level, noise removal is performed for both RGB-D images and LiDAR cloud points by implementing Thresholding based Adaptive Median Filtering (TAMF) algorithm. In the second level, we perform point to voxel conversion for the LiDAR point clouds and fuse both RGB-D images and 3D LiDAR point clouds for better segmentation. Secondly, object segmentation is performed using Improved Mask R-CNN algorithm which creates masked instant bounding boxes by performing semantic segmentation considering both point and local-based information. Estimating pose for the segmented objects using bearing angle-based affine transformation method to extract features efficiently. Thirdly, Feature extraction is performed from the segmented 3D objects by considering high and low-level features using Attention-based YOLO V4 Tiny network and fusing both features for reducing the feature redundancy. Finally, based on the extracted feature the objects are classified as static and dynamic, and 3D bounding boxes are generated to improve the 3D object detection accuracy. The proposed work is evaluated by KITTI dataset. MATLAB R2020a simulation tool is used to perform simulation for the proposed work. The performance of the 3D object detection is evaluated in terms of several performance metrics such as accuracy, precision, recall, F-measure, computation time, and ROC-AUC curve compared to existing methods.