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@@ -42,36 +42,28 @@ Table1. Average precion (AP%) on test dataset1[4].
 Vessel detection studies conducted on inshore and offshore
 maritime images are scarce, due to a limited availability of
 domain-specific datasets. We addressed this need collecting
-two datasets in the Finnish Archipelago. They consist of im-
-ages of maritime vessels engaged in various operating sce-
-narios, climatic conditions and lighting environments. Vessel
-instances were precisely annotated in both datasets. We eval-
-uated the out-of-the-box performance of three state-of-the-art
+two datasets in the Finnish Archipelago. They consist of images of maritime vessels engaged in various operating scenarios, climatic conditions and lighting environments. Vessel
+instances were precisely annotated in both datasets. We evaluated the out-of-the-box performance of three state-of-the-art
 CNN-based object detection algorithms (Faster R-CNN,
 R-FCN and SSD) on these datasets and compared them
-in terms of accuracy and run-time. The algorithms were pre-
-viously trained on the COCO dataset. We explore their
-performance based on different feature extractors. Further-
-more, we investigate the effect of the object size on the algo-
+in terms of accuracy and run-time. The algorithms were previously trained on the COCO dataset. We explore their
+performance based on different feature extractors. Furthermore, we investigate the effect of the object size on the algo-
 rithm performance. For this purpose, we group all objects in
 each image into three categories (small, medium and large)
 according to the number of occupied pixels in the annotated
 bounding box. Experiments show that Faster R-CNN with
-ResNet101 as feature extractor outperforms the other algo-
-rithms.
+ResNet101 as feature extractor outperforms the other algorithms.
 
 # An Efficient Multi-sensor Fusion Approach for Object Detection in Maritime Environmentt [6]: 
 Robust real-time object detection and tracking are
 challenging problems in autonomous transportation systems
 due to operation of algorithms in inherently uncertain and
-dynamic environments and rapid movement of objects. There-
-fore, tracking and detection algorithms must cooperate with
+dynamic environments and rapid movement of objects. Therefore, tracking and detection algorithms must cooperate with
 each other to achieve smooth tracking of detected objects that
 later can be used by the navigation system. In this paper, we
 first present an efficient multi-sensor fusion approach based on
 the probabilistic data association method in order to achieve
-accurate object detection and tracking results. The proposed ap-
-proach fuses the detection results obtained independently from
+accurate object detection and tracking results. The proposed approach fuses the detection results obtained independently from
 four main sensors: radar, LiDAR, RGB camera and infrared
 camera. It generates object region proposals based on the fused
 detection result. Then, a Convolutional Neural Network (CNN)