Investigating the Performance of ECA-YOLOv5n Model for Appearance Quality Detection in Air Conditioner External Units

In an era of rapid technological advancements, keeping up with the iterative updates of factory technology in Intelligent manufacturing presents a daunting challenge. Against this backdrop, this paper investigates the performance of the ECA-YOLOv5n model, a novel approach for appearance quality detection in air conditioner external units. Building upon an open-source dataset, ECA-YOLOv5n model integrates the ECA-Net attention mechanism into the YOLOv5-nano model. ECA-Net effectively captures channel-wise dependencies and studies discriminative features, allowing for seamless integration into the convolutional neural networks of the YOLOv5 architecture without the need for extensive modifications. Furthermore, the efficient channel attention mechanism of ECA-Net demands fewer parameters and computations in comparison to other attention mechanisms, conferring a significant advantage in terms of computational efficiency. Experimental results show that the newly proposed model achieves approximately 98% precision, 99% recall, and 99% mAP on the dataset. The ECA-YOLOv5n model reduces storage usage and minorly increases detection speed in comparison to other YOLO models.