Detection of Defective Apples Using Learnable Residual Multi-Head Attention Networks Integrated with CNNs

Many traditional fruit vendors still rely on manual sorting to pick out high-quality apples. This process is not only time-consuming but can also damage the apples. Meanwhile, automated detection technology is still in its early stage and lacks full reliability. To improve this technology, we propose a novel method, which incorporates a learnable scaling factor and residual connection to enhance the Multi-Head Attention mechanism. In our approach, a learnable scaling factor is first applied to adjust the attention weights dynamically, and then a residual connection combines the scaled attention output with the original input to preserve essential features from the initial data. By integrating Multi-Head Attention with Convolutional Neural Networks (CNNs) using this method, we propose a lightweight deep learning model called “Learnable Residual Multi-Head Attention Networks Fusion with CNNs” to detect defective apples. Compared to existing models, our proposed model has lower memory usage, shorter training time, and higher detection precision. On the test set, the model achieves an accuracy of 97.5%, a recall of 98%, and a specificity of 97%, along with the lowest detection time of 46 ms. Experimental results show that the proposed model using our method is highly promising for commercial sorting, as it reduces labor costs, increases the supply of high-quality apples, and boosts consumer satisfaction.