Neural networks for object detection often struggle with reliability in real-world conditions, frequently producing false positives or unexpected detections, particularly in complex scenes. We introduce a practical approach to improve robustness by enabling the model to learn from its own mistakes. First, a baseline model is trained on high-quality labeled data. We then systematically gather the errors made by this model during evaluation. These specific incorrect detections are curated and assigned to a dedicated ‘error’ label. By augmenting the original training set with these ‘error’ examples and retraining the model, we explicitly teach it to identify inputs that previously led to failures. The resulting model learns to classify these difficult or ambiguous cases into the ‘error’ category, significantly reducing false positives without compromising performance on well-defined objects. Experimental results validate this iterative learning strategy as an effective way to boost model reliability for demanding applications like monitoring and control.