# Enhanced BLIP-2 Optimization Using LoRA for Generating Dashcam Captions
## 4 Experiment
SPICE evaluates the semantic content of captions by parsing them into scene graphs and comparing their objects, attributes, and relationships. It computes an F-score based on matching semantic tuples between the candidate and reference. \[\text{SPICE}=\frac{2\cdot\text{Precision}\cdot\text{Recall}}{\text{Precision}+ \text{Recall}}. \tag{9}\]  
SPICE is particularly useful for assessing how well the generated caption captures the meaning and structure of the scene. As evident in Table 2, both baseline models BLIP-2 and LLaVA exhibited low SPICE, BLEU and CIDEr scores, which indicated that these models had limited capability in generating contextually rich captions for driving environment scenes. The BLEU-4 score for BLIP-2 remained at 0.0117, and LLaVA performed even worse (0.0075), which indicated poor fluency and coherence of the generated captions. Similarly, the SPICE score, which is used for evaluating the semantic structure and relevance, was only 0.1002 for BLIP-2 and 0.0637 for LLaVA, further highlighting their incapability to adapt to driving environments. The BLEU score trends in Figure 4 show a clear upward trajectory as LoRA configurations improve. The fine-tuned enhanced BLIP-2 consistently outperformed the baseline BLIP-2, with superior fluency and lexical diversity of driving environment scene captions. Notably, as the LoRA rank (\(r\)) and scaling factor (\(a\)) increased, the BLEU-4 score improved, with the highest setting (r = 32, a = 64) having a score of 0.1609; this score was approximately 14 times that of the baseline BLIP-2. Enhanced BLIP-2, fine-tuned with LoRA, showed substantial improvements across all evaluation metrics. For instance, BLEU-4 scores improved significantly, reaching 0.1318 with r = 8 and a = 16, and the score further increased to 0.1609 with r = 32 and a = 64. Similarly, CIDEr scores showed a clear upward trend, increasing from 0.0340 (BLIP-2) to 0.2458 (enhanced BLIP-2, r = 32, a = 64). Clearly, fine-tuning resulted in the model generating driving environment scene captions that were closer to human descriptions. The SPICE score also saw a major boost, peaking at 0.4642, confirming the improved semantic consistency in driving environment scene captions. The improvements in CIDEr and SPICE scores, apparent in Figure 5, further show the effectiveness of LoRA-based fine-tuning for domain adaptation. The CIDEr score, which indicates the informativeness of captions, increased from 0.0340 (BLIP-2) to 0.2458 (enhanced BLIP-2, r = 32, a = 64). This suggests that fine-tuned models can produce captions that better capture the details of complex driving environments. Similarly, the SPICE score, which measures semantic correctness and scene understanding, shows a consistent rise, peaking at 0.4642, indicating that models fine-tuned using LoRA generated more contextually relevant captions and showed improved scene comprehension. Figure 4: BLEU score comparison across different models. Higher LoRA settings result in improved BLEU scores, highlighting enhancements in caption fluency and lexical accuracy. These results indicate that LoRA-based fine-tuning effectively enhanced the driving environment scene captioning performance of BLIP-2, improving both linguistic accuracy and contextual relevance. The model with the highest performance (enhanced BLIP-2, r = 32, and a = 64) showed substantial improvements over the baselines, which pointed to the effectiveness of domain-adaptive optimization. Figure 6 presents the results of experiments conducted under a fixed configuration of \(r=8\) and \(\alpha=16\), focusing on the impact of different dropout rates on model performance. The comparison highlights how varying the dropout value influences the effectiveness of LoRA-based optimization across BLEU-4, SPICE, and CIDEr metrics. To determine the optimal dropout rate for LoRA-based optimization, we conducted experiments with three different values: 0.08, 0.05, and 0.02. As shown in Table 4, the configuration with dropout = 0.02 consistently achieved the highest scores across all evaluation metrics.