In this study, a novel deep learning algorithm for object detection, named MelNet, was introduced. MelNet underwent training utilizing the KITTI dataset for object detection. Following 300 training epochs, MelNet attained an mAP (mean average precision) score of 0.732. Additionally, three alternative models -YOLOv5, EfficientDet, and Faster-RCNN-MobileNetv3- were trained on the KITTI dataset and juxtaposed with MelNet for object detection. The outcomes underscore the efficacy of employing transfer learning in certain instances. Notably, preexisting models trained on prominent datasets (e.g., ImageNet, COCO, and Pascal VOC) yield superior results. Another finding underscores the viability of creating a new model tailored to a specific scenario and training it on a specific dataset. This investigation demonstrates that training MelNet exclusively on the KITTI dataset also surpasses EfficientDet after 150 epochs. Consequently, post-training, MelNet's performance closely aligns with that of other pre-trained models.

本研究介绍了一种名为MelNet的新颖深度学习算法用于目标检测，该算法使用KITTI数据集进行训练，并在训练300个时期后得到了0.732的mAP得分。结果突显了在某些情况下使用迁移学习的有效性，并且还表明通过仅在KITTI数据集上训练MelNet可以在150个时期后超过EfficientDet，并且在训练后MelNet的性能与其他预训练模型相当。

MelNet：一种实时的深度学习目标检测算法