Abstract
Convolution Neural Network (CNN)-based object detection models have achieved unprecedented accuracy in challenging detection tasks. However, existing detection models (detection heads) trained on 8-bits/pixel/channel low dynamic range (LDR) images are unable to detect relevant objects under lighting conditions where a portion of the image is either under-exposed or over-exposed. Although this issue can be addressed by introducing High Dynamic Range (HDR) content and training existing detection heads on HDR content, there are several major challenges, such as the lack of real-life annotated HDR dataset(s) and extensive computational resources required for training and the hyper-parameter search. In this paper, we introduce an alternative backwards-compatible methodology to detect objects in challenging lighting conditions using existing CNN-based detection heads. This approach facilitates the use of HDR imaging without the immediate need for creating annotated HDR datasets and the associated expensive retraining procedure. The proposed approach uses HDR imaging to capture relevant details in high contrast scenarios. Subsequently, the scene dynamic range and wider colour gamut are compressed using HDR to LDR mapping techniques such that the salient highlight, shadow, and chroma details are preserved. The mapped LDR image can then be used by existing pre-trained models to extract relevant features required to detect objects in both the under-exposed and over-exposed regions of a scene. In addition, we also conduct an evaluation to study the feasibility of using existing HDR to LDR mapping techniques with existing detection heads trained on standard detection datasets such as PASCAL VOC and MSCOCO. Results show that the images obtained from the mapping techniques are suitable for object detection, and some of them can significantly outperform traditional LDR images.
Highlights
The introduction of Convolution Neural Networks (CNN) has brought about a complete paradigm shift in object recognition and detection, which has been a major challenge in computer vision [1]
To accurately detect salient objects in challenging and/or extreme lighting conditions. This can be attributed to the fact that existing detection models are typically trained on generic object detection datasets comprising mostly of well-exposed and/or moderately lit 8/bits/pixel/channel Low Dynamic Range (LDR) images
In this paper, we propose a robust methodology for object detection in extreme and/or challenging lighting conditions while ensuring backward compatibility with most current detection models trained on existing low dynamic range (LDR) datasets
Summary
The introduction of Convolution Neural Networks (CNN) has brought about a complete paradigm shift in object recognition and detection, which has been a major challenge in computer vision [1]. To accurately detect salient objects in challenging and/or extreme lighting conditions. This can be attributed to the fact that existing detection models are typically trained on generic object detection datasets comprising mostly of well-exposed and/or moderately lit 8/bits/pixel/channel Low Dynamic Range (LDR) images. In these images, a large portion of the scene information is captured as midtones with little or no information in the under-exposed or over-exposed regions. Current detection models trained on existing datasets are unable to extract salient features from scenes with challenging lighting conditions that are typically encountered in real-world scenarios
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