Adaptive Optimization Strategies for Gigapixel Object Detection

GigaPixel-level computer vision tasks recently become new research hotspots, due to the development of photography. Object detection, as a basic, common, but challenging task, undoubtedly received the most attention. However, most of the research focused on the efficiency improvements for the super resolution of the scenarios. They tend to design relevant network modules or inference strategies to help split the whole image into smaller patches for efficient computation. Differently from them, We proposed three optimization strategies that can maintain efficient computation while also ensuring the accuracy of model inference strategies. The strategies are Anchor-Split Sample Strategy, GPU Memory Optimizations and Two-Phase Adaptive Inference Strategy. Anchor-Split Sample Strategy can help train the detectors within 8 hours on the PANDA detection datasets. GPU Memory Optimizations can help train the DETA model with Swin-Large backone on a consumer GPU card like RTX 3080 with only costing 18G memory. Two-Phase Adaptive Inference Strategy, Without the extra training of the additional network modules or complex strategies, can obtain 74% mAP and 82% AR500 performance with only 1.5h cost on the 15W Power Jetson Orin AGX card. Compared with the state-of-the-art methods, our methods can boost the performance by 20% percentage.