RoadNetv2：Real-Time Algorithm for Highway Weak Abandoned Objects Detection

Highway abandoned objects easily cause car out of control, resulting in traffic accidents. In order to solve the problems of low complexity of highway abandoned objects dataset and low detection accuracy and high FLOPS calculation of highway abandoned objects detection algorithm at present, a simulation scenario dataset expansion method and a RoadNetV2 highway abandoned objects detection algorithm are proposed. Simulation scenario dataset expansion method uses similar datasets to expand the scene simulation. RoadNetV2 highway abandoned objects detection algorithm adopts light- focus shallow information enhancement module and C3_CD feature extraction model as main components of Backbone, adopts CoordConv and custom Conv combination method to reduce the complexity of neck, and adopts multi-weight balance calculation strategy to assist Alpha-CIOU to weak objects efficient position regression. Experimental results show that compared with the current series of YOLO algorithms, the FLOPS calculation of RoadNetV2 highway abandoned objects detection algorithm is reduced by 14.54×109 at most to 12.4×109, and mAP（mean average precision）is improved by 3.5 percentage points to 61.1%, weight files are only 8.70 MB less by 4.98 MB. RoadNetV2 highway abandoned objects detection algorithm can meet the deployment requirements of embedded edge devices and mobile devices. Combined with self-developed inspection equipment, RoadNetV2 can solve the slack caused by manual inspection to a certain extent. © The Author(s) 2023.