  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.
