# Shadow Hunter: Low-Illumination Object-Detection Algorithm
###### Abstract  
Recently, object detection, which is focused on images with normal illumination levels, has achieved great success. However, the accuracy of object detection is reduced in suboptimal environments due to the images plagued by noise and low contrast. For boosting the performance of object-detection tasks under low-illumination conditions, we propose three modules for improvement: (1) the low-level feature attention (LFA) module learns to focus on the regional feature information of the object in the low-illumination environment, highlighting important features and filtering noisy information; (2) the feature fusion neck (FFN) obtains enriched feature information by fusing the feature information of the feature map after backbone; (3) the context-spatial decoupling head (CSDH) enables the classification head to focus on contextual semantic information so that the localization head obtains richer spatial details. Extensive experiments show that our algorithm realizing end-to-end detection shows good performance in low-illumination images. object detection; low illumination; attention mechanism; feature fusion +
Footnote †: journal: Applied  
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Footnote 1: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; [EMAIL_ADDRESS]  
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Footnote 1: School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin 541004, China; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (Y.H.) 2
Footnote 1: School of Computer ScienceMost of the current studies on low-illumination treated image enhancement as a pre-processing step, and [6] proposed a low-illumination object-detection algorithm combining deep convolutional adversarial generation network and Faster RCNN to generate normal illumination-like images by adversarial generation network. To enhance the detection performance, feature fusion and multi-scale pooling were incorporated into Faster R-CNN. The human eye, on the other hand, is insufficient for directly evaluating the mean average precision (mAP) of object detection. Moreover, when enhancing low-illumination images, algorithms may lead to noise and the loss of important image features potentially. Consequently, achieving a higher detection mAP becomes challenging. To mitigate these detrimental effects, recent research has focused on directly optimizing the entire object-detection network. These efforts aim to minimize the negative impact on object detection in low-illumination environments and enhance overall detection performance. For example, [PERSON]. [7] introduced the context fusion and feature pyramid module based on RFP-Net [8] to improve the ability of feature extraction for low-light images. However, the major determinant of detection performance is light intensity, and detection performance cannot be significantly enhanced in the event of severe underexposure. To address the above shortcomings, this paper investigates an end-to-end low-illumination object-detection algorithm with higher accuracy, a simpler structure, and the ability to output detection results on low-illumination images. In our approach, we introduce a low-level feature attention (LFA) module into the backbone architecture. This module generates feature maps with varying perceptual fields using different kernel sizes. By performing channel concatenation, we are able to learn the significance of each channel in the feature map, facilitating effective information fusion. Then, we suggest a feature fusion neck (FFN) after the backbone that can aggregate both high-level and low-level features. At this stage, we utilized a structure similar to the feature pyramid, to enable mutual learning among feature maps from different layers, improving the feature expression ability of each feature map and enabling the better fusion of rich low-level feature information at the neck part. Figure 1: Common object-detection algorithms for detecting conditions in low-illumination environments, for which is easy to see that the results are not good enough.