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.

翻译：本研究提出了一种名为MelNet的新型目标检测深度学习算法。该算法采用KITTI数据集进行目标检测训练，经过300轮训练后，MelNet的平均精度均值（mAP）达到0.732。此外，研究在KITTI数据集上训练了YOLOv5、EfficientDet和Faster-RCNN-MobileNetv3三种替代模型，并与MelNet进行目标检测性能对比。结果凸显了特定场景下迁移学习的有效性——值得注意的是，基于主流数据集（如ImageNet、COCO和Pascal VOC）预训练的模型展现了更优性能。另一发现验证了针对特定场景构建新模型并基于专用数据集训练的可行性：实验表明，仅使用KITTI数据集训练的MelNet在150轮训练后即超越EfficientDet。最终训练完成后，MelNet的性能与其他预训练模型高度接近。