4 research outputs found
High-Performance Fine Defect Detection in Artificial Leather Using Dual Feature Pool Object Detection
In this study, the structural problems of the YOLOv5 model were analyzed
emphatically. Based on the characteristics of fine defects in artificial
leather, four innovative structures, namely DFP, IFF, AMP, and EOS, were
designed. These advancements led to the proposal of a high-performance
artificial leather fine defect detection model named YOLOD. YOLOD demonstrated
outstanding performance on the artificial leather defect dataset, achieving an
impressive increase of 11.7% - 13.5% in AP_50 compared to YOLOv5, along with a
significant reduction of 5.2% - 7.2% in the error detection rate. Moreover,
YOLOD also exhibited remarkable performance on the general MS-COCO dataset,
with an increase of 0.4% - 2.6% in AP compared to YOLOv5, and a rise of 2.5% -
4.1% in AP_S compared to YOLOv5. These results demonstrate the superiority of
YOLOD in both artificial leather defect detection and general object detection
tasks, making it a highly efficient and effective model for real-world
applications
YOLOCS: Object Detection based on Dense Channel Compression for Feature Spatial Solidification
In this study, we examine the associations between channel features and
convolutional kernels during the processes of feature purification and gradient
backpropagation, with a focus on the forward and backward propagation within
the network. Consequently, we propose a method called Dense Channel Compression
for Feature Spatial Solidification. Drawing upon the central concept of this
method, we introduce two innovative modules for backbone and head networks: the
Dense Channel Compression for Feature Spatial Solidification Structure (DCFS)
and the Asymmetric Multi-Level Compression Decoupled Head (ADH). When
integrated into the YOLOv5 model, these two modules demonstrate exceptional
performance, resulting in a modified model referred to as YOLOCS. Evaluated on
the MSCOCO dataset, the large, medium, and small YOLOCS models yield AP of
50.1%, 47.6%, and 42.5%, respectively. Maintaining inference speeds remarkably
similar to those of the YOLOv5 model, the large, medium, and small YOLOCS
models surpass the YOLOv5 model's AP by 1.1%, 2.3%, and 5.2%, respectively
Discharge current modes of high power impulse magnetron sputtering
Based on the production and disappearance of ions and electrons in the high power impulse magnetron sputtering plasma near the target, the expression of the discharge current is derived. Depending on the slope, six possible modes are deduced for the discharge current and the feasibility of each mode is discussed. The discharge parameters and target properties are simplified into the discharge voltage, sputtering yield, and ionization energy which mainly affect the discharge plasma. The relationship between these factors and the discharge current modes is also investigated