## 3 Experimental and Analysis
### Comparison Experiments
#### 3.5.3 Comparison Experiment with Detection Algorithms  
To effectively assess the detection performance of the proposed algorithm, model weight size was added as a lightweight evaluation metric. The improved algorithm was compared with one-stage algorithms SSD, YOLOv5s, YOLOv7 [21], and YOLOv8s, as well as the two-stage algorithm Faster R-CNN and popular lightweight algorithms. The results are shown in Table 8.  
From Table 8, it is evident that early SSD and Faster R-CNN, as well as the recently proposed YOLOv7, have large parameter and computational requirements, which hinder their deployment on hardware platforms. Compared with YOLOv8s, the proposed algorithm improved mAP by 2.1% and reduced the model size by 8.0 MB. Compared with lightweight algorithms like YOLOv3-tiny and YOLOv7-tiny, our algorithm achieved significant improvements in mAP by 19.2% and 9.0%, respectively. Additionally, it decreased GFLOPs by 16.2% and 17.4%, respectively, reduced Params by 41.5% and 15.8%, respectively, and decreased the model size by 6.5 MB and 1.6 MB, respectively. Compared with YOLOv-tiny, the algorithm proposed in this paper has slightly higher complexity but demonstrated greater advantages in detection accuracy and model size metrics. At the same time, compared with YOLOv5s, the proposed algorithm reduced the number of parameters by 27.8% and GFLOPs by 31.9%, while achieving an mAP of 98.3% and a model size of only 10.1 MB. To more thoroughly demonstrate the effectiveness of the proposed algorithm, we compared it with improved algorithms from the related literature while keeping the dataset and experimental parameters unchanged. Compared with references [22] and [23], although the proposed algorithm had slightly fewer parameters and lower computational complexity, it achieved improvements in accuracy of 4.6% and 5.5%, respectively. Reference [24] had lower model memory usage, but its mAP was 91.7%, which was 6.6 percentage points lower than the mAP of the proposed algorithm. In summary, the CER-YOLOv5s algorithm balanced better detection accuracy and model lightweighting, meeting the needs for engineering-vehicle detection in complex environments and making it more suitable for deployment on devices with limited computational resources. The algorithms mentioned above were visualized and analyzed based on the same test set, as shown in Figure 14. Due to cluttered background interference, YOLOX-tiny detected the excavator twice in the third image, while YOLOv7-tiny mistakenly detected the red container in the second image as a truck. The SSD and Faster R-CNN algorithms, although free from false detections, exhibited poor performance in detecting small targets in the third image. While YOLOv7 and YOLOv5s show improved detection accuracy, their confidence scores for detecting small objects were below 0.9. In contrast, our algorithm demonstrated better detection performance overall without encountering false positives. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **mAP/\% \(\uparrow\)** & **Params/M \(\downarrow\)** & **GFLOPs \(\downarrow\)** & **FPS \(\uparrow\)** & **Model Size/MB \(\downarrow\)** \\ \hline SSD & 84.5 & 24.15 & 61.2 & 21.70 & 92.1 \\ Faster R-CNN & 87.4 & 136.77 & 369.8 & 2.42 & 108.0 \\ YOLOv5s & 95.4 & 7.03 & 16.0 & 60.66 & 13.7 \\ YOLOv7 & 84.4 & 37.21 & 105.2 & 27.47 & 71.3 \\ YOLOv8s & 96.2 & 11.13 & 28.4 & 51.02 & 18.1 \\
**YOLOv3-tiny** & **79.1** & **8.68** & **13.0** & **94.43** & **16.6** \\
**YOLOv7-tiny** & **89.3** & **6.03** & **13.2** & **87.37** & **11.7** \\
**YOLOX-tiny** & **96.6** & **5.06** & **6.45** & **61.49** & **19.42** \\
**Reference** [22] & **93.7** & **4.11** & **7.3** & **72.10** & **17.9** \\
**Reference** [23] & **92.8** & **4.53** & **9.6** & **53.42** & **6.4** \\
**Reference** [24] & **91.7** & **7.17** & **11.3** & **64.61** & **3.9** \\
**CER-YOLOv5s** & **98.3** & **5.08** & **10.9** & **63.53** & **10.1** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental comparison of detection algorithms.