Object detection and classification are vital in image processing and computer vision. Despite substantial progress in image object detection, challenges remain with visible spectrum imaging under adverse conditions. Thermal infrared cameras offer benefits such as operating in complete darkness, sensitivity to illumination changes, resilience to shadows, and penetration through haze and smog. These advantages enable reliable object recognition during both day and night. However, many popular object detection algorithms struggle with ground-based TIR images due to various factors such as small object size, poor image quality, obstacles, and variable illumination conditions. This study proposes the YOLO (You Only Look Once) model using YOLOv8m as the framework to detect small objects and address related issues. The dataset of small objects has been collected using a “Shot” thermal imaging camera. The customized TIR (Thermal Infrared) dataset of small objects was classified into four classes: keys, bolts, ipieces, and coins. YOLOv8m was used for training, enhancement, and implementation of the dataset. The results demonstrate that the YOLOv8m model was flexible and efficient in recognizing these small objects accurately. To evaluate the performance of YOLOv8m, comparisons were made with other algorithms focusing on recognition performance. The YOLOv8m model attained the highest mean average precision (mAP) of 94.5%, surpassing YOLOv5 with 84.8% and Faster R-CNN with 81.9%. In summary, the findings show that the YOLOV8m model is the most efficient for object detection compared to other models.