Unmanned aerial vehicles (UAVs), particularly quadcopters, are increasingly employed in diverse applications due to their manoeuvrability and affordability. However, their susceptibility to GPS jamming and the need for skilled operators limit their operational resilience. This paper presents a new design of an Image-Based Visual Servoing (IBVS) control system for autonomous quadcopter interception, utilizing only a monocular camera and an Inertial Measurement Unit (IMU). The system features a Multi-Axis PID Controller with acceleration limiting and a virtual plane projection method to decouple pitch and vertical motions, thereby enhancing interception accuracy and maintaining target visibility within the drone’s field of view (FOV). Furthermore, a perception module is designed to run Yolo-v8n and CSRT in parallel, followed by a Kalman filter, to deliver an accurate and robust representation of the target within the image, operating at 18 frames per second in Simulation-in-the-Loop (SITL) environments. Comprehensive SITL experiments and comparative analyses against recent IBVS algorithms demonstrate that the proposed proposed system achieves a 19% reduction in circular error probable (CEP) for static targets and superior performance in diverse scenarios involving moving targets. These findings validate the effectiveness of the proposed IBVS approach, offering a reliable and scalable solution for autonomous UAV interception in GPS-denied environments with potential applications in security, surveillance, and conflict zones.