A Novel Electronic Chip Detection Method Using Deep Neural Networks

Abstract

Electronic chip detection is widely used in electronic industries. However, most existing detection methods cannot handle chip images with multiple classes of chips or complex backgrounds, which are common in real applications. To address these problems, a novel chip detection method that combines attentional feature fusion (AFF) and cosine nonlocal attention (CNLA), is proposed, and it consists of three parts: a feature extraction module, a region proposal module, and a detection module. The feature extraction module combines an AFF-embedded CNLA module and a pyramid feature module to extract features from chip images. The detection module enhances feature maps with a region intermediate feature map by spatial attentional block, fuses multiple feature maps with a multiscale region of the fusion block of interest, and classifies and regresses objects in images with two branches of fully connected layers. Experimental results on a medium-scale dataset comprising 367 images show that our proposed method achieved mAP0.5=0.98745 and outperformed the benchmark method.

Chip detection algorithm based on lightweight E-YOLOv5 convolutional neural network

“…Although the above scholars have proposed efficient defect detection methods, it is found in the actual production process that the feature detection points are difficult to detect correctly, such as the chip surface pattern being complex. The reflection when taking the chip photo leads to a high error detection rate; It is difficult to run the high precision and high speed detection algorithm on weak equipment, which seriously affects the production efficiency [13].…”