  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.
