YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide - Code World

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

Enterprise 2023-09-29 20:50:45 views: null

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Origin blog.csdn.net/m0_47867638/article/details/132528482

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

YoloV8 improvement strategy: SPD-Conv is added to YoloV8, allowing small targets to have nowhere to hide

Optimize and improve the YOLOv5 algorithm: add the SPD-Conv module to make small targets have nowhere to hide - (super detailed)

YoloV8 improvement strategy: NWD new paradigm of small target detection, helping YoloV5 and V8 to violently increase their points on small targets

Yolov8 loss function improvement: Wasserstein Distance Loss, helping small targets to increase points

YoloV8 improvement strategy: RepViT improves YoloV8, lightweight Block helps YoloV8 achieve better mobility

YoloV8 improvement strategy: Based on hierarchical attention FasterViT, let YoloV8 achieve a leap in performance

YoloV8 improvement strategy: lightweight Slim Neck creates the ultimate YoloV8

YOLOv8/YOLOv7/YOLOv5/YOLOv4/Faster-rcnn series algorithm improvement [NO.69] Improvement of small targets in remote sensing image target detection CATnet (ContextAggregation module)

YoloV8 improvement strategy: FastVit and YoloV8 are perfectly integrated, and the YoloV8 network is reconstructed with heavy parameters (first release on the entire network)

YoloV8 improvement strategy: Diverse Branch Block improves YoloV8 and continues to fight against wolves on the heavy parameter structure.

yolov5 improves spd-conv

YoloV8 improvement strategy: CoordConv adds coordinates to the convolution, making it spatially aware.

YoloV8 improvement strategy: Shape-IoU, considering the measurement of border shape and scale

YoloV8 improvement strategy: Intel parameter-oriented efficient dynamic convolution KernelWarehouse, YoloV8’s top-notch package

YoloV8 improvement strategy: deformable large-core attention D-LKA, YoloV8’s super-large sauce latte

YoloV8 improvement strategy: BAM bottleneck attention module | BAM detailed explanation and code comments | CBAM companion article | Effective points

Raising point artifact: SPD-Conv based on Yolov8, low-resolution images and small objects have obvious rising points

Raising point artifact: SPD-Conv based on Yolov8, low-resolution images and small objects have obvious rising points

Improving YOLOv5/YOLOv8: Reproduction combined with plug-and-play | Efficient multi-scale attention (EMA), the module becomes a small helper for YOLOv5 improvement

YOLOV5/YOLOV7/YOLOV8 Improvement Column

What improvements does YOLOv8 have over the previous generation?

YOLOv8’s latest improvement series: Optimize CIoU to Wise-IoU to help improve detection performance and effectively improve small target detection accuracy! ! ! Far ahead! ! ! ! !

[YOLOv7/YOLOv5 series improvement NO.53] Integrating the ECVBlock module in the CFPNet network to improve the detection ability of small targets

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