YOLOv8‐ESW: An Improved Oncomelania hupensis Detection Model

Traditional Oncomelania hupensis detection relies on human eye observation, which results in reduced efficiency due to easy fatigue of the human eye and limited individual cognition, an improved YOLOv8 O. hupensis detection algorithm, YOLOv8‐ESW(expectation–maximization attention [EMA], Small Target Detection Layer, and Wise‐IoU), is proposed. The original dataset is augmented using the OpenCV library. To imitate image blur caused by motion jitter, salt and pepper, and Gaussian noise were added to the dataset; to imitate images from different angles captured by the camera in an instant, affine, translation, flip, and other transformations were performed on the original data, resulting in a total of 6000 images after data enhancement. Considering the insufficient feature fusion problem caused by lightweight convolution, We present the expectation–EMA module (E), which innovatively incorporates a coordinate attention mechanism and convolutional layers to introduce a specialized layer for small target detection (S). This design significantly improves the network's ability to synergize information from both superficial and deeper layers, better focusing on small target O. hupensis and occluded O. hupensis . To tackle the challenge of quality imbalance among O. hupensis samples, we employ the Wise‐IoU (WIoU) loss function (W). This approach uses a gradient gain distribution strategy and improves the model convergence speed and regression accuracy. The YOLOv8‐ESW model, with 16.8 million parameters and requiring 98.4 GFLOPS for computations, achieved a mAP of 92.74% when tested on the O. hupensis dataset, marking a 4.09% improvement over the baseline model. Comprehensive testing confirms the enhanced network's efficacy, significantly elevating O. hupensis detection precision, minimizing both missed and false detections, and fulfilling real‐time processing criteria. Compared with the current mainstream models, it has certain advantages in detection accuracy and has reference value for subsequent research in actual detection.