This study proposes a method based on lightweight convolutional neural
networks (CNN) and generative adversarial networks (GAN) for apple ripeness and
damage level detection tasks. Initially, a lightweight CNN model is designed by
optimizing the model's depth and width, as well as employing advanced model
compression techniques, successfully reducing the model's parameter and
computational requirements, thus enhancing real-time performance in practical
applications. Simultaneously, attention mechanisms are introduced, dynamically
adjusting the importance of different feature layers to improve the performance
in object detection tasks. To address the issues of sample imbalance and
insufficient sample size, GANs are used to generate realistic apple images,
expanding the training dataset and enhancing the model's recognition capability
when faced with apples of varying ripeness and damage levels. Furthermore, by
applying the object detection network for damage location annotation on damaged
apples, the accuracy of damage level detection is improved, providing a more
precise basis for decision-making. Experimental results show that in apple
ripeness grading detection, the proposed model achieves 95.6\%, 93.8\%, 95.0\%,
and 56.5 in precision, recall, accuracy, and FPS, respectively. In apple damage
level detection, the proposed model reaches 95.3\%, 93.7\%, and 94.5\% in
precision, recall, and mAP, respectively. In both tasks, the proposed method
outperforms other mainstream models, demonstrating the excellent performance
and high practical value of the proposed method in apple ripeness and damage
level detection tasks.

本研究提出了一种基于轻量级卷积神经网络（CNN）和生成对抗网络（GAN）的方法，用于苹果成熟度和损伤程度检测任务，通过优化模型的深度和宽度以及应用先进的模型压缩技术设计了轻量级 CNN 模型，引入注意机制提高对象检测任务的性能，利用 GAN 生成逼真的苹果图像扩展训练数据集，改善样本不平衡和样本量不足问题，通过在受损苹果上应用目标检测网络进行损伤位置注释，提高了损伤程度检测的准确性，在苹果成熟度和损伤程度检测任务中，该方法的性能优于其他主流模型，具有卓越的性能和高实用价值。

高效苹果成熟度和损伤评估：具有 GAN 和注意力机制的轻量级检测模型

Efficient Apple Maturity and Damage Assessment: A Lightweight Detection  Model with GAN and Attention Mechanism

We examine how the choice of data-side attributes for two important visual
tasks of image classification and object detection can aid in the choice or
design of lightweight convolutional neural networks. We show by experimentation
how four data attributes - number of classes, object color, image resolution,
and object scale affect neural network model size and efficiency. Intra- and
inter-class similarity metrics, based on metric learning, are defined to guide
the evaluation of these attributes toward achieving lightweight models.
Evaluations made using these metrics are shown to require 30x less computation
than running full inference tests. We provide, as an example, applying the
metrics and methods to choose a lightweight model for a robot path planning
application and achieve computation reduction of 66% and accuracy gain of 3.5%
over the pre-method model.

我们研究了在图像分类和目标检测的两个重要视觉任务中，选择数据方面属性如何帮助选择或设计轻量级卷积神经网络。通过实验，我们展示了四个数据属性 - 类别数目、目标颜色、图像分辨率和目标尺度如何影响神经网络模型的大小和效率。通过基于度量学习的内部和外部类别相似度度量，来指导这些属性的评估从而实现轻量级模型。使用这些度量进行评估，其计算量比完全进行推理测试少 30 倍。我们提供了一个应用这些评估和方法来选择一个轻量级模型用于机器人路径规划应用的示例，其计算量减少了 66%，精度提高了 3.5%。

轻量级卷积神经网络的数据端效率

Data-Side Efficiencies for Lightweight Convolutional Neural Networks

"Lightweight convolutional neural networks" is an important research topic in
the field of embedded vision. To implement image recognition tasks on a
resource-limited hardware platform, it is necessary to reduce the memory size
and the computational cost. The contribution of this paper is stated as
follows. First, we propose an algorithm to process a specific network
architecture (Condensation-Net) without increasing the maximum memory storage
for feature maps. The architecture for virtual feature maps saves 26.5% of
memory bandwidth by calculating the results of cross-channel pooling before
storing the feature map into the memory. Second, we show that cross-channel
pooling can improve the accuracy of object detection tasks, such as face
detection, because it increases the number of filter weights. Compared with
Tiny-YOLOv2, the improvement of accuracy is 2.0% for quantized networks and
1.5% for full-precision networks when the false-positive rate is 0.1. Last but
not the least, the analysis results show that the overhead to support the
cross-channel pooling with the proposed hardware architecture is negligible
small. The extra memory cost to support Condensation-Net is 0.2% of the total
size, and the extra gate count is only 1.0% of the total size.

本文介绍了如何通过交叉通道池化技术减少轻量级卷积神经网络的计算复杂度，提高对象检测的准确性，并采用 Condensation-Net 算法，实现在有限的硬件资源平台上的图像识别任务。