Object Detection and Ship Classification Using YOLOv5

Abstract

Using a public dataset of images of maritime vessels provided by Analytics Vidhya, manual annotations were made on a subsample of images with Roboflow using the ground truth classifications provided by the dataset. YOLOv5, a prominent open source family of object detection models that comes with an out-of-the-box pre-training on the Common Objects in Context (COCO) dataset, was used to train on annotations of subclassifications of maritime vessels. YOLOv5 provides significant results in detecting a boat. The training, validation, and test set of images trained YOLOv5 in the cloud using Google Colab. Three of our five subclasses, namely, cruise ships, ROROs (Roll On Roll Off, typically car carriers), and military ships, have very distinct shapes and features and yielded positive results. Two of our subclasses, namely, the tanker and cargo ship, have similar characteristics when the cargo ship is unloaded and not carrying any cargo containers. This yielded interesting misclassifications that could be improved in future work. Our trained model resulted in the validation metric of mean Average Precision (mAP@.5) of 0.932 across all subclassification of ships.

Object detection and ship classification using YOLO and Amazon Rekognition

“…Object detection and classification research has been done using a varied number of techniques, algorithms, and models. This paper is a continuation of the work started by Brown et al (1) on maritime object detection and classification using YOLOv5. Brown et al (1) used the same maritime dataset and created the five vessel subclasses used in this paper, using them to train a detection model with YOLOv5 and achieving an overall mAP (0.5) of 0.919.…”

“…(i) The Roboflow environment of the original work performed by Brown et al (1) was recreated. This means the (2) and (3) dataset of 8000 vessel images was uploaded and the original annotated subset consisting of roughly 1500 annotated images was imported into the Roboflow project.…”

“…Brown et al (1) created an object detection model for large maritime vessels using a publicly available dataset of maritime images from (2) and (3). This dataset was imported into Roboflow (4), where a subset was annotated with bounding boxes and labeled using five different classification labels for large seafaring vessels ("Container Ships, " "Cargo Ships, " "Military Ships, " "Tankers, " and Roll-On-Roll-Off ships or "RORO").…”

“…This annotated subset was used to train an object recognition and classification model by utilizing the open-source Ultralytics (5,6) vision AI library YOLOv5 (7,8). Their results produced a model that yielded an overall 0.892 average precision rate and 0.902 average recall rate; however, while the model proved accurate in detecting vessels with "Cruise Ship, " "Military Ship, " and "RORO" labels with 93, 98, and 98% correct classification percentages, respectively, it performed considerably worse in classifications of "Container Ships" and "Tankers" with a correct classification percentage of 86 and 72%, respectively (1).…”

“…This paper is unique in its comparative approach of different classification models using a large maritime vessel image database. In contrast to the original paper (1), where a single YOLO model was utilized, it creates three additional classification models and provides an efficiency and accuracy comparison between them to arrive at the best possible results. Furthermore, it utilizes a premium service in Amazon Rekognition to provide a different alternative to the YOLO approach.…”