A significant challenge in the field of object detection lies in the system's performance under non-ideal imaging conditions, such as rain, fog, low illumination, or raw Bayer images that lack ISP processing. Our study introduces "Feature Corrective Transfer Learning", a novel approach that leverages transfer learning and a bespoke loss function to facilitate the end-to-end detection of objects in these challenging scenarios without the need to convert non-ideal images into their RGB counterparts. In our methodology, we initially train a comprehensive model on a pristine RGB image dataset. Subsequently, non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This comparison employs the Extended Area Novel Structural Discrepancy Loss (EANSDL), a novel loss function designed to quantify similarities and integrate them into the detection loss. This approach refines the model's ability to perform object detection across varying conditions through direct feature map correction, encapsulating the essence of Feature Corrective Transfer Learning. Experimental validation on variants of the KITTI dataset demonstrates a significant improvement in mean Average Precision (mAP), resulting in a 3.8-8.1% relative enhancement in detection under non-ideal conditions compared to the baseline model, and a less marginal performance difference within 1.3% of the mAP@[0.5:0.95] achieved under ideal conditions by the standard Faster RCNN algorithm.

翻译：目标检测领域面临的一个重大挑战是系统在非理想成像条件下的性能表现，例如雨雾天气、低照度环境或未经ISP处理的原始Bayer图像。本研究提出"特征校正迁移学习"这一创新方法，通过融合迁移学习与定制化损失函数，实现这些复杂场景下的端到端目标检测，且无需将非理想图像转换为RGB格式。该方法首先在纯净RGB图像数据集上训练完整模型，随后通过比较非理想图像与初始理想RGB模型的特征图进行处理。这种比较采用扩展区域新型结构差异损失函数（EANSDL），该新型损失函数通过量化特征相似度并将其融入检测损失，从而优化模型在不同条件下的目标检测能力。整个流程通过直接特征图校正实现，充分体现了特征校正迁移学习的核心理念。在KITTI数据集变体上的实验验证表明，该方法使平均精度均值（mAP）显著提升：相比基线模型，非理想条件下的检测性能相对提升3.8%-8.1%，且与标准Faster RCNN算法在理想条件下取得的mAP@[0.5:0.95]性能差异保持在1.3%以内。