  Object identification is one of the most fundamental and difficult issues in
computer vision. It aims to discover object instances in real pictures from a
huge number of established categories. In recent years, deep learning-based
object detection techniques that developed from computer vision have grabbed
the public's interest. Object recognition methods based on deep learning
frameworks have quickly become a popular way to interpret moving images
acquired by various sensors. Due to its vast variety of applications for
various computer vision tasks such as activity or event detection,
content-based image retrieval, and scene understanding, academics have spent
decades attempting to solve this problem. With this goal in mind, a unique deep
learning classification technique is used to create an autonomous object
detecting system. The noise destruction and normalising operations, which are
carried out using gaussian filter and contrast normalisation techniques,
respectively, are the first steps in the study activity. The pre-processed
picture is next subjected to entropy-based segmentation algorithms, which
separate the image's significant areas in order to distinguish between distinct
occurrences. The classification challenge is completed by the suggested Hybrid
Optimized Dense Convolutional Neural Network (HODCNN). The major goal of this
framework is to aid in the precise recognition of distinct items from the
gathered input frames. The suggested system's performance is assessed by
comparing it to existing machine learning and deep learning methodologies. The
experimental findings reveal that the suggested framework has a detection
accuracy of 0.9864, which is greater than current techniques. As a result, the
suggested object detection model outperforms other current methods.
