# A YOLOW Algorithm of Water-Crossing Object Detection
###### Abstract  
Accurately identifying and locating water-crossing objects is of utmost importance for environmental protection. However, traditional detection algorithms often exhibit poor anti-interference performance and low detection accuracy in complex environments. To address these issues, this paper proposes a YOLOW algorithm based on a one-stage object detection algorithm for the automatic identification of water objects. The proposed algorithm incorporates two new modules, namely the SPDCS module and the SPPAUG module, to improve the model's performance. Specifically, the SPDCS module retains all information in the channel dimension, thereby enhancing the model's detection accuracy and recognition ability for water-crossing objects. The SPPAUG module performs multiscale feature fusion, which further improves the model's detection accuracy and recognition ability. Moreover, the C2f module is introduced from YOLOv8 to increase the detection speed. Experimental results on a water-floating object dataset demonstrate that the improved YOLOW model outperforms the standard YOLOv5s algorithm, especially in water-crossing object detection. This research has significant implications for environmental monitoring and protection. object detection; water-crossing objects; deep learning; YOLOv5 +
Footnote †: journal: Medical  
reflections on object detection accuracy. These factors can introduce significant noise into the image, making it harder to distinguish between objects and their surroundings. Radar [3,4] and infrared [5] detection are commonly used techniques for detecting objects related to water. Compared with RGB imaging, these methods have limitations. Radar detection relies on reflected echo signals, while infrared detection relies on the thermal radiation emitted by objects. Both technologies lack the ability to provide visual information, such as the shape and color of objects. In addition, radar and infrared detection may not provide high-resolution images and may be challenged to distinguish different types of objects in water. Environmental factors such as weather conditions and water turbulence can also affect their accuracy. Another limitation of these technologies is the high equipment cost compared with conventional RGB cameras. Since the introduction of the AlexNet [6] model, CNNs (convolutional neural networks) have shown remarkable performance in various computer vision tasks. In image classification, several famous CNN models have emerged, such as AlexNet, VGGNet [7], and ResNet [8]. In terms of object detection, a series of excellent models have been developed, including R-CNN series [9; 10; 11; 12; 13; 14], YOLO series [15; 16; 17; 18; 19; 20; 21; 22; 23; 24], SSD [25], EfficientDet [26], and FCOS [27; 28]. These algorithms have advantages such as automatic learning, good feature representation, and high-precision detection. However, existing object detection algorithms are not effective in extracting features relevant to water-crossing environments and detecting objects in low-resolution or blurry areas. To address these challenges, a new algorithm called YOLOW is proposed in this paper. YOLOW builds upon YOLOv5 by introducing an SPDCS (Space-to-Depth-Conv-Se) module, proposing an SPPAUG module based on the YOLOv5 SPPF module, and introducing a C2f module from YOLOv8. The contributions of this paper can be summarized as follows:  
1. A new module called SPDCS has been designed based on the existing SPD (Space-to-Depth) module. Compared with the SPD module, the SPDCS module downsamples the original features and performs convolution and attention operations on the original features to further obtain more channel count features. In this way, a balance is achieved between obtaining new features and preserving initial features, thus improving data analysis and processing capabilities. Compared with the original Yolov5 algorithm, using this innovative module can achieve five times the number of channels.