Object detection and classification in thermal imaging is crucial for a range that operates continuously, such as security, inspection, emergency assistance, and border tracking. When applying several state-of-the-art object detection algorithms to ground-based thermal imaging, the main obstacles include inconsistencies in target size, low-quality images, obstruction, and varying illuminating conditions. This study analyzes the ability of deep-learning algorithms to examine various objects within ground-based thermal images. The novelty of this study lies in its utilization of two network architectures, MobileNetV2 and EfficientNet-B0, which are pre-trained on ImageNet visual images. These networks are typically employed for traditional optical images, but MobileNetv2in this research, are repurposed and trained on custom datasets of thermal images that have been created using the “Seek Shot” camera. This modification aims to test their performance and suitability in analyzing thermal images, expanding their usefulness beyond their original purpose. Standard metrics such as precision, the experiment, the 5-fold cross-validation used to address the bias of the models. The results demonstrate that the MobileNetV2 model achieves the highest detection accuracy of 96.5% for car classes. The research explores alternative deep learning models Faster RCNN and RetinaNet to compare object detection accuracy in thermal datasets. The results showed that out of all the models, MobileNetv2 had the highest mean average precision (mAP) with 92.6% and EfficientNet-B0 had better accuracy than the other two state-of-the-art models, while RetinaNet had the lowest mAP score of 6.57%.