Deep Neural Network-based Object Detectors exhibit robustness to standard perturbations techniques such as pixel attacks, adversarial noise, blurring and occlusion, yet may remain vulnerable to many emerging threats. Formal methods can provide stronger guarantees than conventional testing or validation, and have been used to verify properties like robustness, sensitivity and safety. While progress has been made on simple neural networks mostly concerning image classification models, extending them to different architectures and tasks, such as object detection, remains challenging due to the increased structural complexity and dynamic output space. This work proposes a novel extension to the ImageStar verification framework to enable scalable verification of robustness for object detectors. Using state-of-the-art solvers and a custom dataset, we demonstrate the efficacy of our approach in identifying vulnerabilities and providing stronger robustness guarantees.