# YOLO-PWSL-Enhanced Robotic Fish: An Integrated Object Detection System for Underwater Monitoring
###### Abstract
[PERSON] of the Graduate School of Science and Engineering, Doshisha University, proposed a bionic fish robot with two joints, which dramatically improved the robot's speed and energy efficiency [22]. In terms of aquaculture monitoring systems, Md. [PERSON] designed an IoT system that can automatically measure parameters such as pH, temperature, oxygen level, etc., but could not measure the various parameters uniformly and made some errors [23]. [PERSON] et al. proposed a LoRaWAN-based aquaculture monitoring technique using the Kalman filter to effectively improve data accuracy, but it is too expensive [24]. [PERSON] et al. proposed an energy-efficient routing protocol for underwater sensor networks in the ocean that can effectively avoid path loss [25]. In terms of visual perception technology, underwater robots rely on computer vision algorithms to achieve target detection and recognition. In target detection, deep learning is primarily divided into two categories: one-stage and two-stage. One-stage algorithms primarily include SSD [26] and the YOLO [27] series. Representative two-stage algorithms include R-CNN [28], Fast-RCNN [29], and Faster-RCNN [30]. The original YOLO algorithm achieves end-to-end unified prediction by transforming object detection into a regression problem. Subsequent research has further optimized its underwater adaptability [31]. For example, in 2024, [PERSON] et al. from Harbin Engineering University proposed a real-time underwater fish detection and recognition algorithm based on the lightweight design of the CBAM-YOLO network, which effectively identified and detected six different fish species. However, its computational requirements are high, which makes it unsuitable for real-time underwater target detection tasks [32]. In 2021, [PERSON] et al. proposed an algorithmic model combining DCGAN + SSD + PSO to improve the performance of all aspects of the model for undersea target biodiversity data collection. However, due to folding, obscuring, and various lighting conditions, the underwater background changes, and the dataset did not fully take this into account [33]. In 2024, the Guangdong Provincial Laboratory of Marine Science and Engineering in the South proposed an underwater target detection network with a cavity deformation convolution and a two-branch masking attention mechanism--Underwater-Yolo, an underwater target detection network with good detection capability in closed and complex underwater environments--but the model still performs poorly in high-density target and occlusion scenarios [34]. [PERSON] et al. proposed a dead fish identification model based on the YOLOv5s model, which showed improved detection accuracy by 4.5% compared to the baseline model, but there has been little research on the identification of dead fish at night or in low light conditions [35]. The integration of visual perception and bionic machines in underwater robotics will allow for future development in underwater robotics [36; 37]. For example, [PERSON] et al. from China Agricultural University designed a new bionic manta ray for sea cucumber identification, achieving 94.5% detection accuracy, but its underwater capabilities are limited [38]. These studies indicate that the existing systems still have significant shortcomings in terms of target detection capabilities and multi-functional integration in complex environments (such as dense, obstacle-filled environments) and are highly susceptible to environmental interference. Therefore, building on previous work, our research focuses on enhancing the target detection algorithm's recognition capabilities in complex environments and expanding the functional diversity of robotic fish. Here, we propose a low-cost, real-time detection and object recognition-capable underwater biomimetic fish robot system. Its objective is to achieve the real-time detection of water quality parameters and adaptation in real time to the environmental conditions required by different fish species. This system lays the foundation for real-time monitoring in aquaculture.