IEMAyoloViT:an Underwater Target Detection Algorithm Based on Improved YOLOv8
The effectiveness is hampered by challenges arising from optical imaging techniques,which are adversely affected by light attenuation and scattering.These factors collectively contribute to a reduction in image quality and target resolution,thereby introducing impediments to the underwater target detection tasks.To address these challenges,an efficient underwater target detection model,denoted as IEMAyoloViT,is introduced.The proposed model incorporates an enhanced YOLOv8 algorithm,referred to as YOLOViT,which is based on refinements made to the Vision Transformer(ViT)backbone.Additionally,a C2f module that integrates Efficient Multi-scale Attention(EMA)is also incorporated.This architectural augmentation addresses concerns related to attention dispersion during the extraction of target features.Furthermore,the model leverages an improved Inner-CIoU loss function and incorporates auxiliary boundaries at various scales to expedite the process of bounding box regression.The results show that,on the Underwater Robot Professional Contest(URPC)2021 dataset,the devised IEMAyoloViT attains an mAP50 of 83.2%,exhibiting a notable improvement of 9.2%over YOLOv8.Furthermore,the mAP50:95 metric also surpasses YOLOv8 by 1.0%,which proves the effectiveness and application potential of IEMAyoloViT in underwater target detection.