## 4 Discussion  
To ascertain the superior efficiency of the enhanced VEW-YOLOv8n algorithm, derived from YOLOv8n, it was compared with prevailing two-stage and single-stage target detection algorithms. The two-stage category included FasterRCNN, while the single-stage category included lightweight algorithms like YOLOv3-tiny [34], YOLOv5n [35], YOLOv6n [36], and YOLOv8n, along with high-precision, medium-large algorithms such as YOLOv8 m [37], and YOLOv3. Algorithms with larger convolutional kernels, namely YOLOv8n-InceptionNext and SSD target detection algorithms, were also compared. The YOLO-series algorithms used in these comparisons, similar to VEW-YOLOv8n, employed the same training strategy, incorporating methods like DFL, Anchor Free, Decoupled-Head, CIoU, and TaskAlignedAssigner. The comparative performance evaluation results of VEW-YOLOv8n with mainstream and improved algorithms are displayed in Table 3 (two-stage target detection algorithm) and Table 4 (single-stage target detection algorithm). In Table 3, the two-stage models, based on migration learning and using pre-trained weights, recorded lower AP values compared to VEW-YOLOv8n, with a larger number of parameters and slower inference speeds. Table 4 shows the comparison among single-stage target detection algorithms. VEW-YOLOv8n stood out with the smallest model size (4.6 MB), least number of parameters (2.28 \(\times\) 10\({}^{6}\)), lowest GFLOPS (5.9), while achieving the highest average accuracy ([EMAIL_ADDRESS] at 93.9%, [EMAIL_ADDRESS]:0.95 at 75.8%). Compared to high-precision, medium-large target detection algorithms, VEW-YOLOv8n attained similar average accuracy but with far fewer parameters, lower GFLOPS, and smaller model size. Against algorithms with large convolutional kernels, VEW-YOLOv8n surpassed YOLOv8n-InceptionNext in all aspects. The performance comparison of various YOLO family algorithms, including YOLOv3-tiny, YOLOv3-tiny, YOLOv5n, YOLOv6n, YOLOv8n, YOLOv8n, and YOLOv8n-InceptionNext, with VEW-YOLOv8n on two evaluation metrics, GFLOPS and [EMAIL_ADDRESS], is illustrated in Figure 16, highlighting VEW-YOLOv8n's minimal computational intensity and maximal average accuracy. Figure 15: Comparison of the performance of the bounding box loss function. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **[EMAIL_ADDRESS]/\%** & **[EMAIL_ADDRESS]:0.95/\%** & **GFLOPS** & **Size/MB** & **Speed/ms** \\ \hline SSD (Pretrained) & 92.4 & 70.9 & 344.4 & 184.3 & 8.9 \\ FasterRCNN (Pretrained) & 91.5 & 70.5 & 206.68 & 315.1 & 25.8 \\ VEW-YOLOv8n & 95.6 & 76.2 & 5.9 & 4.6 & 7.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of VEW-YOLOv8n with two-stage target detection algorithms. From three performance indicators--the number of parameters (params), computational volume (GFLOPS), and model size--the apple appearance grading detection algorithm (VEW-YOLOv8n) proposed in this study is confirmed as a low-complexity, lightweight target detection algorithm. Figure 18 shows VEW-YOLOv8n's superiority with the smallest number of params, least computational volume, and smallest model size, rendering it suitable for edge-end deployment in initial screening and categorization of apples. In order to have a more intuitive feeling of the detection performance of the VEW-YOLOv8n algorithm, standard test images of four apple types (HEALTHY, BLOTCH, ROT, and SCAB) were used. The detection effect of VEW-YOLOv8n was compared with YOLOv3-tiny, YOLOv5n, YOLOv6n, and YOLOv8n, and as depicted in Figure 19, the results indicate that VEW-YOLOv8n excels in detecting all four apple types. Specifically, in single-target detection, VEW-YOLOv8n demonstrates higher confidence than the other models. In multi-target detection, VEW-YOLOv8n outperforms the alternatives, with YOLOv5n, YOLOv6n, and YOLOv3-tiny exhibiting varying levels of detection omission.