## 3 Results
### Detection Along the Paths  
The detection results were evaluated by comparing them with manually labeled data. Matching was based on the distance between the mean points of the objects projected onto the XY plane. During the evaluation process, for each LIDAR scan, the objects detected by the algorithm and those labeled by a human were categorized into three classes: correct, missed, and wrong. Objects detected by the algorithm that had a corresponding object in the human-labeled set within a distance of 25 cm were classified as correct (true positives, TP). Human-labeled objects that did not have corresponding objects detected by the algorithm were marked as missed (false negatives, FN). Finally, objects detected by the algorithm that did not have a match in the human-labeled set were marked as wrong detections (false positives, FP). It is expected that the correctness of the detection depends on the volume of the object present in the sensor's field of view and that it will increase with the number of LIDAR points corresponding to the idlers. Positions of the detected objects from each class in the robot's local coordinates for both paths are presented in Figure 11. The additional lines in the figure indicate the borders of areas with the same theoretical number of active LIDAR channels. This number is defined as the number of LIDAR measurement surfaces that can intersect a vertical line between 0 and 0.7 m, assuming no occlusions are present. This corresponds to the maximum theoretical number of LIDAR channels capable of registering the idler frame at a given location. It may be observed that correct detections appear in the first row of idler supports, as the supports in the second row are occluded by the belts and other closer objects. Also, as expected, better results are observed in areas with a higher number of active LIDAR channels. To determine the area of the effective operation of the algorithm, an evaluation was performed for different regions of interest. The region of interest, denoted as X+, is defined as the area where the theoretical number of active LIDAR channels is greater than or equal to X and bounded by horizontal lines between the robot and the middle of the belt. A channel is considered theoretically active if the surface of the cone for this channel intersects the object, even if due to occlusions and other factors no actual measurement of the object for that channel is registered in the scan. A summary of the results for various regions of interest is shown in Figure 12.  
Figure 10: Examples of detection. It may be observed that the areas with the number of active channels of four or less have only a minimal influence on the results. Starting from regions of interests restricted to five or more active channels, the number of objects decreases, but the percentage of correct detection grows. The biggest change is observed when limiting output to the areas of six and more active channels. Further restrictions have a smaller influence, especially for Path A and, at the same time, substantially decrease the number of objects in the region of interest. Figure 13 presents the distribution of the detection results for the region of interest limited to six or more active channels, clipped to the first row of supports, i.e., between the robot path and the conveyor's belt. Figure 11: Spatial distribution of detection results in robot local coordinates and unrestricted range. (**a**) Along Path A. (**b**) Along Path B.  
Figure 12: Detection results in areas with various theoretical numbers of active LIDAR channels. (**a**) Along Path A. (**b**) Along Path B.  
Figure 13: Spatial distribution of detection results in robot local coordinates in region limited to 6 and more LIDAR planes. (**a**) Along Path A. (**b**) Along Path B. A comparison of the detection results in the whole scan and in the limited region is presented in Table 3. It may be noted that the accuracy within the region 6+ is above 83% for Path A and 85% for Path B.