# An Improved Lightweight Model for Defect Detection on Paths in Images
## 3 Results and Analysis
### Comparison of Performance of Different Models  
This study performed comparison trials using many popular object identification algorithms to assess the improved model's performance [30]. The selected models include not only the mainstream YOLO series target detectors based on CNNs, such as the earlier YOLOv5, YOLOv6 [31], and YOLOv7, and the latest YOLOv9 and YOLOv10, but also multiple versions of Transformer-based target detectors, such as RT-DETR-I [32] and RT-DETR-resnet50. The same dataset was used in the studies, and the same experimental setup  
Figure 6: A comparison was performed to assess the mAP values of the ACD-YOLOv8 model in relation to the original model. (**a**) Comparison of [EMAIL_ADDRESS], and (**b**) comparison of [EMAIL_ADDRESS]-0.95. Figure 7: The ACD-YOLOv8 model and the YOLOv8 model’s confusion matrix. (**a**) YOLOv8’s confusion matrix. (**b**) ACD-YOLOv8’s confusion matrix. was used. For comparison, one image from a vehicle-mounted camera and two drone-captured road condition photos from the RDD2022 dataset were chosen for identification and detection. The detection results are shown in the picture, where different defect types are indicated by annotations and different defect categories are represented by colored detection boxes. For instance, D00 denotes longitudinal cracks, D10 transverse cracks, D20 mesh cracks, D40 potholes, and Repair denotes road repairs. Drone-captured photos were used in two sets of studies, and Figure 8 displays the detection outcomes of several models. In the first experiment, none of the models exhibited any missed or false detection, and all models demonstrated good recognition capabilities for the D10 class of defects. Although all targets were successfully detected for the D00 class, there was a significant variation in defective detection effects, with the YOLOv6s model being the most affected. Due to the slightly poorer detection performance of YOLOv8s for the D20 class, the second scenario was an experiment targeting the detection of D20 class defects. The results showed that only the YOLOv5s and ACD-YOLOv8 detected the presence of D20 road defects, but although the YOLOv5s model could detect defects in this experiment, its IoU value was very low at 0.3, indicating that the predicted bounding box was highly inaccurate. On the other hand, ACD-YOLOv8 was successfully detected in this experiment at an IoU threshold of 0.5. With fewer misses and more precise projected bounding boxes than previous models, the ACD-YOLOv8 model put forward in this study performed exceptionally well in both trials. Figure 9 displays the detection outcomes of several models (gathering vehicle camera pictures) from Experiment 2. All models were able to successfully recognize the targets, with YOLOv5s, YOLOv6s, and the YOLOv8s, YOLOv9c models showing the same detection results, while YOLOv10s showed an increase in performance in detecting the Repair type of defect but a significant decrease in detection capability for D40 defects. Outperforming the prior models, our suggested ACD-YOLOv8s model successfully identified both targets and generated more accurate bounding boxes throughout the target identification phase. The combined results of the two experiments indicate that in various scenarios, in comparison to previous models, the ACD-YOLOv8s model exhibits reduced miss rates, lower false detection rates, and greater detection accuracy. These findings provide evidence of our model's outstanding performance in different scenarios, significantly enhancing target detection. As presented in Table 9, with an F1-score of 0.83 and a [EMAIL_ADDRESS] accuracy of 87.4%, the ACD-YOLOv8 model significantly outperformed other object identification models. These findings demonstrate the enhanced performance and stability of the ACD-YOLOv8 model. Furthermore, in addition to improving [EMAIL_ADDRESS] and F1-score, the enhancements made by ACD-YOLOv8 in terms of lightweighting are even more pronounced, with the model reducing its parameter count by 33.33% compared to the original YOLOv8s.