Lane Line Type Recognition Based on Improved YOLOv5

Liu, Boyu; Wang, Hao; Wang, Yongqiang; Zhou, Congling; Cai, Lei

doi:10.3390/app131810537

Cited by 1 publication

(1 citation statement)

References 39 publications

(45 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, microscopic inspection is heavily dependent on the skill level of the operator and necessitates specialized expertise. Additionally, electrical performance tests may fail to identify minor physical anomalies that do not immediately affect functionality but could potentially lead to long-term degradation [3,4].…”

Section: Introductionmentioning

confidence: 99%

Enhanced YOLOv8 with BiFPN-SimAM for Precise Defect Detection in Miniature Capacitors

Li,

Ye,

Zhou

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

In the domain of automatic visual inspection for miniature capacitor quality control, the task of accurately detecting defects presents a formidable challenge. This challenge stems primarily from the small size and limited sample availability of defective micro-capacitors, which leads to issues such as reduced detection accuracy and increased false-negative rates in existing inspection methods. To address these challenges, this paper proposes an innovative approach employing an enhanced ‘you only look once’ version 8 (YOLOv8) architecture specifically tailored for the intricate task of micro-capacitor defect inspection. The merging of the bidirectional feature pyramid network (BiFPN) architecture and the simplified attention module (SimAM), which greatly improves the model’s capacity to recognize fine features and feature representation, is at the heart of this methodology. Furthermore, the model’s capacity for generalization was significantly improved by the addition of the weighted intersection over union (WISE-IOU) loss function. A micro-capacitor surface defect (MCSD) dataset comprising 1358 images representing four distinct types of micro-capacitor defects was constructed. The experimental results showed that our approach achieved 95.8% effectiveness in the mean average precision (mAP) at a threshold of 0.5. This indicates a notable 9.5% enhancement over the original YOLOv8 architecture and underscores the effectiveness of our approach in the automatic visual inspection of miniature capacitors.

show abstract

Section: Introductionmentioning

confidence: 99%