# A YOLOW Algorithm of Water-Crossing Object Detection
## 2 Related Work  
### Real-Time Object Detectors  
The emergence of the convolutional neural network (CNN) has brought revolutionary changes to the field of object detection. In object detection tasks, CNN models are usually divided into two types: one-stage and two-stage. The one-stage detector directly detects locations with dense sampling, skipping the region recommendation step. This method typically uses a single network to predict the location and category of an object. The two-stage detector first provides rough region suggestions and then uses a head network to refine them to obtain the accurate position and category of the object. This method typically involves two stages of operation, namely generating region suggestions and classifying and regressing region suggestions. Generally speaking, a one-stage object detection model uses a CNN-based backbone network to extract image features and uses detection heads to predict the category and bounding box of each object. In addition, in order to detect objects of different sizes, additional layers will be added to combine features of multiple scales, thereby generating feature representations with rich semantics. Currently, real-time object detectors based on YOLO and FCOS are in a leading position, including models such as YOLOv5, YOLOv7 [23], and YOLOv8 [24]. Typically, a two-stage object detector consists of two main steps: generating candidate regions and classifying and refining bounding boxes. In the first stage, effective techniques such as selective search, R-CNN series, or edge boxes are used to generate candidate regions. These candidate regions typically have multiple scales and aspect ratios to represent areas where objects may appear. Usually, a one-stage detector is faster than a two-stage detector. These one-stage detector models need to have faster and stronger network architecture, more effective feature integration [29; 30; 31; 32; 33; 34; 35], more accurate detection methods [27; 28; 36], more robust loss function [37; 38; 39; 40; 41; 42], more effective tag allocation methods [43; 44; 45; 46; 47], and more effective training strategies to become the most advanced real-time object detectors. Therefore, a one-stage detector is more suitable for real-time object detection. YOLOv5 is an object detection algorithm based on deep learning, whose core idea is to divide the entire image into several grid units, each of which is responsible for detecting the presence of objects and predicting information such as object position, category, and confidence. Compared with previous versions, YOLOv5 further improves detection performance and speed by improving network structure and training strategies. YOLOv5 uses CSPDarknet as the backbone network. It is a lightweight and efficient network structure that can extract high-level semantic information from images. YOLOv5 also uses specific anchor boxes to improve detection accuracy. These anchor boxes are predefined rectangular boxes with different sizes and aspect ratios, which are used to detect objects of different sizes and shapes. In addition, YOLOv5 also adopts various techniques for enhancing feature representation, such as adaptive convolution and multiscale training strategies. Adaptive convolution is a technique that can automatically adjust the size of convolution kernels, enabling the extraction of finer features on objects of different scales. The multiscale training strategy is to train on images at different scales, making the network have better scale invariance and generalization ability. YOLOv5 has achieved remarkable results in various application domains, including object detection, pedestrian detection, and vehicle detection. It possesses low computational complexity and high real-time performance, with great potential for practical applications. This paper proposes a water-crossing environment object detection algorithm called YOLOW based on the YOLOv5 algorithm.