Erin Becker Rázuri, Yang Yang, Elaine Tinius, Danica Kalling Knight
Adaptation of a trauma-informed intervention to prevent opioid use among youth in the legal system
Journal of substance use and addiction treatment
identification sugarcane stem nodes generally dependent highperformance recognition equipment sugarcane seed precutting machines inefficient accordingly study proposes novel lightweight architecture detection sugarcane stem nodes based yolov5 framework named gyolov5sss firstly study removes cbs c3 structures end backbone network fully utilize shallowlevel feature information enhances detection performance sugarcane stem nodes simultaneously eliminates 32 times downsampled branches neck structure 20x20 detection heads prediction end reducing model complexity secondly ghost lightweight module introduced replace conventional convolution module bottleneck structure reducing models complexity finally study incorporates simam attention mechanism enhance extraction sugarcane stem node features without introducing additional parameters improvement aims enhance recognition accuracy compensating loss precision due lightweight modifications experimental results showed average precision improved network sugarcane stem node identification reached 976 06 higher yolov5 baseline network meanwhile model size 26mb 1129340 parameters 72g flops representing respective reductions 82 84 544 compared mainstream onestage target detection algorithms yolov4tiny yolov4 yolov5n yolov6n yolov6s yolov7tiny yolov7 gyolov5sss achieved respective average precision improvements 129 507 36 21 12 3 04 sugarcane stem nodes recognition meanwhile model size compressed 889 989 333 72 929 788 963 respectively compared similar studies gyolov5sss enhanced recognition accuracy also considered model size demonstrating overall excellent performance aligns requirements sugarcane seed precutting machines