Computer vision technology has notably been an important area of research. In this research project, we have integrated voice recognition technology with its application in real time. Object detection and classification is a popular computer vision task that involves the identification and localization of objects within images or videos. It has a crucial contribution in various sectors, such the industry, healthcare, transportation, and the military. Our objective is to implement YOLOv7, a popular object detection framework, to count specific object categories including Cars, buses, motorcycles, and Persons using a human voice as input. Pre-designed voice recognition and voice signature recognition algorithms were employed to convert the human voice to a text command to be sent to the object detection and counting model while utilizing a speaker’s identity verification before sending the text command. We have evaluated the performance of the system on diverse images taken from various angles, considering challenging scenarios such as occlusions and varying scales. The results demonstrate the effectiveness and reliability of YOLOv7 in precisely counting instances of the specified object classes, even during nighttime and on crowded roads and highways from far distances. This methodology has the potential to be used on drones to detect and count in an aerial photography view.