论文标题
汽车零件评估：应用实例实例分割模型以识别车辆零件
Automotive Parts Assessment: Applying Real-time Instance-Segmentation Models to Identify Vehicle Parts
论文作者
论文摘要
自动化汽车伤害评估的问题提出了汽车维修和损害评估行业的主要挑战。该域名有几个应用领域，包括汽车评估公司（例如汽车租赁和车身商店）到汽车保险公司的意外损害评估。在车辆评估中，损坏可能会采取任何形式，包括划痕，次要凹痕和主要凹痕。评估区域经常具有很大的噪音，例如污垢，油脂，石油或匆忙，这使得准确的识别具有挑战性。此外，对特定部分的识别是维修行业的第一步，即具有准确的劳动力和一部分评估，在这种情况下，不同的汽车模型，形状和尺寸的存在使该任务使机器学习模型的表现更加具有挑战性。为了应对这些挑战，本研究探索并应用了各种实例分割方法来评估最佳性能模型。 这项工作的范围集中在两种实例分割模型的类型上，因为它们的工业意义，即sipmask和yolact。这些方法是针对先前报道的汽车零件数据集（DSMLR）以及从当地汽车维修工作室提取的内部策划数据集进行评估的。与其他实例实例机制相比，基于YOLACT的零件定位和分割方法表现良好，MAP为66.5。对于研讨会维修数据集，SIPMASK ++报告了对象检测的更好精度，其映射为57.0，其结果为AP_IOU = .50和AP_IOU = .75分别报告72.0和67.0，而Yolact则发现YOLACT是44.0和2.6的AP_S和2.6的对象检测类别的AP_S的表现更好。
The problem of automated car damage assessment presents a major challenge in the auto repair and damage assessment industry. The domain has several application areas ranging from car assessment companies such as car rentals and body shops to accidental damage assessment for car insurance companies. In vehicle assessment, the damage can take any form including scratches, minor and major dents to missing parts. More often, the assessment area has a significant level of noise such as dirt, grease, oil or rush that makes an accurate identification challenging. Moreover, the identification of a particular part is the first step in the repair industry to have an accurate labour and part assessment where the presence of different car models, shapes and sizes makes the task even more challenging for a machine-learning model to perform well. To address these challenges, this research explores and applies various instance segmentation methodologies to evaluate the best performing models. The scope of this work focusses on two genres of real-time instance segmentation models due to their industrial significance, namely SipMask and Yolact. These methodologies are evaluated against a previously reported car parts dataset (DSMLR) and an internally curated dataset extracted from local car repair workshops. The Yolact-based part localization and segmentation method performed well when compared to other real-time instance mechanisms with a mAP of 66.5. For the workshop repair dataset, SipMask++ reported better accuracies for object detection with a mAP of 57.0 with outcomes for AP_IoU=.50and AP_IoU=.75 reporting 72.0 and 67.0 respectively while Yolact was found to be a better performer for AP_s with 44.0 and 2.6 for object detection and segmentation categories respectively.
