# Shadow Hunter: Low-Illumination Object-Detection Algorithm
## 2 Related Work
### Object Detection  
Object detection is an important research direction in the field of computer vision, which aims to locate and identify the objects in an image. Traditional object-detection algorithms primarily depend on manually designed feature extractors and classifiers, including Haar features, HOG features, SIFT features, SVM classifiers, and AdaBoost classifiers, among others. Although these algorithms have certain effects in some simple scenarios, they perform poorly in complex scenarios and face difficulty with handling problems such as multi-scale, multi-pose, and occlusion. Since the introduction of R-CNN [19], which employed convolutional neural networks for object detection, object-detection methods have been categorized into two main groups: one-stage methods and two-stage methods. One-stage methods, also known as single-shot detectors, are object-detection algorithms that predict bounding box coordinates and classification scores directly in a single forward pass of the neural network. These methods offer faster inference times and simpler implementation compared to two-stage methods. The one-stage object-detection algorithm is designed to detect objects in images. In contrast to the two-stage object-detection algorithm, it eliminates the need for separate stages of generating candidate frames and predicting object categories. Instead, the one-stage algorithm performs a single forward pass, generating predictions for candidate frames by simultaneously outputting object categories, labels, and their corresponding scores. This approach enables fast and accurate object detection. The one-stage object-detection algorithm offers several advantages, including high computational efficiency, fast inference speed, and excellent performance when handling large-scale image datasets. Currently, one of the most well-known and widely used one-stage object-detection algorithms is You Only Look Once(YOLO) [4], which is faster and suitable for real-time object-detection scenarios because it directly predicts bounding boxes and categories instead of using sliding windows or candidate regions. In addition, the Single Shot MultiBox Detector (SSD) [20] and RetinaNet [21] algorithm is also a commonly used one-stage object-detection algorithm, which has a slight advantage over YOLO in terms of accuracy. The speed and accuracy of one-stage object-detection algorithms have been greatly enhanced with the integration of deep learning techniques in computer vision. Two-stage object-detection algorithms typically follow a two-step process: candidate frame proposal and object classification. In these algorithms, the initial stage involves generating candidate frames from the input image, while the subsequent step focuses on classifying and localizing the targets within those frames. One of the most popular two-stage object-detection methods is Faster R-CNN [3], which introduced region proposal networks (RPN) that identify object proposals and classify them using a shared convolutional network. However, Faster R-CNN still suffers from slow region proposal steps, leading to the emergence of Mask R-CNN [22]. This method incorporates a masked convolutional layer to efficiently generate bounding boxes, enabling effective object detection and segmentation. As a result, the overall performance of the algorithm is enhanced. Another variant of two-stage object detection is the Cascade R-CNN [23] algorithm, which improves the performance of the Faster R-CNN by introducing multiple stages of classification and refinement, leading to better accuracy and precision in object detection. The algorithm's object-detection strategy involves using a series of RPNs that select high-quality proposal regions, and a cascade of classifiers that refine the detection results. In addition to the approaches mentioned above, significant research efforts have been devoted to advancing anchor-free techniques in object-detection algorithms. Among these, CenterNet [24] stands out as one of the most effective methods.