Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions

• URL: http://arxiv.org/abs/2404.11214v2
• Date: Fri, 19 Apr 2024 14:26:06 GMT
• Title: Feature Corrective Transfer Learning: End-to-End Solutions to Object Detection in Non-Ideal Visual Conditions
• Authors: Chuheng Wei, Guoyuan Wu, Matthew J. Barth,
• Abstract summary: "Feature Corrective Transfer Learning" is a novel approach to facilitate the end-to-end detection of objects in challenging scenarios. Non-ideal images are processed by comparing their feature maps against those from the initial ideal RGB model. This approach refines the model's ability to perform object detection across varying conditions through direct feature map correction.
• Score: 11.90136900277127
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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