CCPPO: Reinforcement learning enhancing confidence for object detection in industrial environments

The proliferation of autonomous systems in industrial and warehousing environments necessitates highly reliable object detection, yet these settings present significant challenges, including severe occlusions, variable illumination, and the prevalence of extremely small objects. Standard deep learning detectors struggle in these high-variance scenes, as their inherent confidence scores are often unreliable, which is unacceptable for safety-critical applications like obstacle avoidance. Furthermore, any viable solution must explicitly satisfy near real-time inference latency constraints. Addressing this gap, this paper introduces Context Confidence Proximal Policy Optimization (CCPPO), a novel reinforcement learning (RL)–based algorithm designed to enhance detector robustness. CCPPO exploits contextual and domain-specific information—reasoning often absent from contemporary neural approaches—to evaluate and refine model predictions. Operating either as a complement to or a replacement for the conventional confidence function, CCPPO leverages RL, analogous to its use in refining Large Language Model outputs, to provide a more assured measure of certainty. We demonstrate the substantial benefits of this approach on the challenging LOCO dataset, showing that CCPPO significantly improves detection performance while maintaining inference latencies compatible with stringent industrial requirements.