Model comparison:图6中对比了image - zoom模型和pool45 - crop模型。 结果表明,图像缩放模型的性能优于pool45 - crop模型。我们假设这种性能损失是由于pool4或pool 5层上的ROI池导致的分辨率损失。而在image - zooms模型中,Pool5的7x7 feature map是直接通过图像上的缩放计算得到的,而在pool45 - crop模型中,Po...
The attention module that is often used in object detection is mainly divided into channel-wise attention and point-wise attention, and the representatives of these two attention models are Squeeze-and-Excitation (SE) [29] and Spatial Attention Module (SAM) [85], respectively. Although SE module...
The attention module that is often used in object detection is mainly divided into channel-wise attention and pointwise attention, and the representatives of these two attention models are Squeeze-and-Excitation (SE) and Spatial Attention Module (SAM), respectively. Although SE module can improve th...
The performance of two object detection models, Faster RCNN and the Single Shot Detector (SSD), were evaluated and compared in terms of weed detection performance using mean Intersection over Union (IoU) and inference speed. It was found that the Faster RCNN model with 200 box pro...
2.1. Object detection models 2.2. Bag of freebies 2.3. Bag of specials 3. Methodology The basic aim is fast operating speed of neural network, in production systems and optimization for parallel computations, rather than the low computation volume theoretical indicator (BFLOP). We present two op...
Object detection is a technique that uses neural networks to localize and classifying objects in images.
2.1. Object detection models 2.2. Bag of freebies 2.3. Bag of specials 3. Methodology The basic aim is fast operating speed of neural network, in production systems and optimization for parallel computations, rather than the low computation volume theoretical indicator (BFLOP). We present two op...
Comparison of different object detection models Performances of several mainstream object detection algorithms are compared, and the results are shown in Table 5. Among the several algorithms other than our proposed method, the Faster-RCNN29, Cascade R-CNN30, YOLOv5, and YOLOv731 algorithms have ...
deep-neural-networkscomputer-visiondeep-learningcomparisonneural-networksyolodeeplearningobject-detectionobjectdetectionyolov3yolov4yolov5yoloryoloxyolov6yolov7yolov8yolo-nasyolov9yolov10 UpdatedOct 31, 2024 The YOLOv11 C++ TensorRT Project in C++ and optimized using NVIDIA TensorRT ...
models. The maximum size of the images used for training the proposed framework is 32×32 pixels. The experiments are conducted using rescaled German Traffic Sign Recognition Benchmark dataset (GTSRB) and downsampled German Traffic Sign Detection Benchmark dataset (GTSDB). Unlike MS COCO and DOTA...