  This paper focuses on improving object detection performance by addressing
the issue of image distortions, commonly encountered in uncontrolled
acquisition environments. High-level computer vision tasks such as object
detection, recognition, and segmentation are particularly sensitive to image
distortion. To address this issue, we propose a novel approach employing an
image defilter to rectify image distortion prior to object detection. This
method enhances object detection accuracy, as models perform optimally when
trained on non-distorted images. Our experiments demonstrate that utilizing
defiltered images significantly improves mean average precision compared to
training object detection models on distorted images. Consequently, our
proposed method offers considerable benefits for real-world applications
plagued by image distortion. To our knowledge, the contribution lies in
employing distortion-removal paradigm for object detection on images captured
in natural settings. We achieved an improvement of 0.562 and 0.564 of mean
Average precision on validation and test data.
