FIR-YOLACT:Fusion of ICIoU and Res2Net for YOLACT on Real-Time Vehicle Instance Segmentation

作     者：Wen Dong Ziyan Liu Mo Yang Ying Wu 

作者机构：College of Big Data and Information EngineeringGuizhou UniversityGuiyang550025China The State Key Laboratory of Public Big DataGuizhou UniversityGuiyang550025China 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2023年第77卷第12期

页      面：3551-3572页

核心收录：

学科分类：0710[理学-生物学] 08[工学] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：supported by the Natural Science Foundation of Guizhou Province(Grant Number:20161054) Joint Natural Science Foundation of Guizhou Province(Grant Number:LH20177226) 2017 Special Project of New Academic Talent Training and Innovation Exploration of Guizhou University(Grant Number:20175788) The National Natural Science Foundation of China under Grant No.12205062. 

主　　题：Instance segmentation real-time vehicle detection YOLACT Res2Net ICIoU 

摘      要：Autonomous driving technology has made a lot of outstanding achievements with deep learning,and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems.The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information,which is more accurate and reliable than object detection.However,the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed.Therefore,this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT,which fuses the ICIoU(Improved Complete Intersection over Union)and Res2Net for the YOLACT algorithm.Specifically,the ICIoU function can effectively solve the degradation problem of the original CIoU loss function,and improve the training convergence speed and detection accuracy.The Res2Net module fused with the ECA(Efficient Channel Attention)Net is added to the model’s backbone network,which improves the multi-scale detection capability and mask prediction accuracy.Furthermore,the Cluster NMS(Non-Maximum Suppression)algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects.The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components.The processing speed reaches 28 FPS,which meets the demands of real-time vehicle instance segmentation.